• Pie Chart: Data represented as a fraction of a circle
• Bar chart: show comparisons between categories of data
• Pareto: Use when there are many problems or causes and you want to focus on the most significant
• Histogram: Bar chart of a frequency distribution
Steam and leaf: a method for showing the frequency with which certain classes of values occur
The document shows performance metrics for various customer service representatives over a period from May 11th to June 9th, including average handle time, transfers percentage, and percentages of honored commitments. Most representatives had average handle times between 4 to 8 minutes, transfers percentages between 20-40%, and over 90% of commitments honored. However, a few outliers had higher or lower values in some metrics.
The document appears to be a research report that includes survey results from respondents of different ages and genders. It includes multiple tables showing responses to questions about reading habits, book purchasing behaviors, magazine reading habits, and preferred genres. The tables break down responses by age and gender across many different reading-related metrics.
This document provides an overview of the Statistics for Management course. The course is full credit over 20 sessions and taught by Dr. Richa Srivastava. Topics covered include qualitative and quantitative data analysis, data collection methods, data visualization techniques like charts and graphs, descriptive statistics like measures of central tendency and dispersion, and shape measures like skewness and kurtosis. Examples of summary statistics and data visualizations are also provided.
We all need to eat and, for a majority of consumers, that involves the chore of grocery shopping, but how and when do most consumers go about it? A survey of 1,000 U.S. adults.
This document summarizes the key topics and concepts covered in Chapter 2 of the 9th edition of the business statistics textbook "Presenting Data in Tables and Charts". The chapter discusses guidelines for analyzing data and organizing both numerical and categorical data. It then covers various methods for tabulating and graphing univariate and bivariate data, including tables, histograms, frequency distributions, scatter plots, bar charts, pie charts, and contingency tables.
This document discusses various methods to evaluate the accuracy of age data, including errors in reported ages. It describes Whipple's Index, Myer's Index, Age Ratios, Age Ratio Scores, Age Specific Sex Ratios, Sex Ratio Scores, and the UN Joint Score. These indices are calculated using population data to identify issues like digit preference, under-enumeration of certain age groups, and inconsistencies in age and sex ratios that indicate errors in the age data. Graphs and examples from India's 2011 census are provided to illustrate the application and interpretation of these evaluation methods.
The document shows performance metrics for various customer service representatives over a period from May 11th to June 9th, including average handle time, transfers percentage, and percentages of honored commitments. Most representatives had average handle times between 4 to 8 minutes, transfers percentages between 20-40%, and over 90% of commitments honored. However, a few outliers had higher or lower values in some metrics.
The document appears to be a research report that includes survey results from respondents of different ages and genders. It includes multiple tables showing responses to questions about reading habits, book purchasing behaviors, magazine reading habits, and preferred genres. The tables break down responses by age and gender across many different reading-related metrics.
This document provides an overview of the Statistics for Management course. The course is full credit over 20 sessions and taught by Dr. Richa Srivastava. Topics covered include qualitative and quantitative data analysis, data collection methods, data visualization techniques like charts and graphs, descriptive statistics like measures of central tendency and dispersion, and shape measures like skewness and kurtosis. Examples of summary statistics and data visualizations are also provided.
We all need to eat and, for a majority of consumers, that involves the chore of grocery shopping, but how and when do most consumers go about it? A survey of 1,000 U.S. adults.
This document summarizes the key topics and concepts covered in Chapter 2 of the 9th edition of the business statistics textbook "Presenting Data in Tables and Charts". The chapter discusses guidelines for analyzing data and organizing both numerical and categorical data. It then covers various methods for tabulating and graphing univariate and bivariate data, including tables, histograms, frequency distributions, scatter plots, bar charts, pie charts, and contingency tables.
This document discusses various methods to evaluate the accuracy of age data, including errors in reported ages. It describes Whipple's Index, Myer's Index, Age Ratios, Age Ratio Scores, Age Specific Sex Ratios, Sex Ratio Scores, and the UN Joint Score. These indices are calculated using population data to identify issues like digit preference, under-enumeration of certain age groups, and inconsistencies in age and sex ratios that indicate errors in the age data. Graphs and examples from India's 2011 census are provided to illustrate the application and interpretation of these evaluation methods.
1) The document contains data on learning achievement test scores from 80 students. It includes the frequency table, histogram, and polygon frequency diagram. It also calculates the mean score of 77.
2) A second dataset is provided with test scores and frequencies totaling 80 students. It calculates the mean score of 77, draws the ogive diagram, and estimates the median score as 77.3.
3) Monthly ticket sales data from 2018 is presented for 12 months totaling 1550 thousand tickets. It converts the sales to percentages and draws a pie chart comparing the monthly sales.
This document provides an overview of different types of variables and methods for summarizing clinical data, including descriptive statistics. It discusses categorical variables like gender and ordinal variables like disease staging. For continuous variables it explains measures of central tendency like mean, median and mode, and measures of variation like range, standard deviation, and interquartile range. Graphs for summarizing univariate data are also covered, such as bar charts for categorical variables and histograms and box plots for continuous variables.
The document provides admissions statistics for Nazarbayev University Foundation Year Programme (NUFYP) from 2010 to 2017. It summarizes the number of applicants, exam takers who met admission criteria, and students accepted each year. It also breaks down admission numbers and test scores by gender, region of Kazakhstan, and NU school. The number of applicants and exam takers generally decreased from 2010 to 2017 while the number accepted fluctuated between 484 and 706.
The document provides admissions statistics for Nazarbayev University Foundation Year Programme (NUFYP) from 2010 to 2017. It summarizes the number of applicants, exam takers who met admission criteria, and students accepted each year. It also breaks down admission numbers and test scores by subject, region, gender, and NU school. The number of applicants and exam takers generally decreased from 2010 to 2017 while the number accepted fluctuated between 484 and 706. Engineering was the most popular school for admitted students throughout this period.
When fitting loss data (insurance) to a distribution, often the parameters that provide a good overall fit will understate the density in the tail.
This method allows one to split the distribution into 2 portions, and use a Pareto distribution to fit the tail.
Presented at the CAS Spring Meeting in Seattle, May 2016.
This document describes Joseph Reiter's analysis of unemployment data from the 2010 Census and 2008-2012 American Community Survey. It discusses: 1) reducing 559 variables down to 58 then selecting a 5-variable model to predict unemployment, 2) comparing models with different transformations of the response variable, 3) evaluating residuals and normality, and 4) concluding the selected model is robust but regional effects were less significant than expected and causation cannot be determined.
This document provides information on percentages, sequences, shapes, mean, median, mode, and range. It includes examples and questions for finding percentages by multiplication, percentage increase and decrease calculations, and formulas for calculating the areas of different shapes including rectangular prisms, triangular prisms, triangles, parallelograms, and trapezoids. It also mentions mean, median, mode, and range but does not provide details on these topics.
The document appears to be a survey containing respondents' answers to questions about experiences with unidentified flying objects (UFOs) and other paranormal phenomena. Some key details:
- 76.22% of respondents reported seeing an intelligently controlled craft in the sky or ground not made by humans.
- 60.40% of those who saw a UFO said that other witnesses also saw it.
- 41.64% believe they possess knowledge about advanced technology or physics they have not learned.
- 80.52% reported experiencing altered states like out of body experiences.
- 56.61% believe they have observed a non-human intelligent entity.
So in summary, the
This document provides an introduction to biostatistics. It defines biostatistics as the application of statistics to biology. Biostatistics involves collecting, presenting, analyzing, and interpreting biological data to make decisions. It also discusses different types of data, methods for presenting data including tables, graphs, and summary statistics. Common graphical methods include histograms, bar charts, and pie charts. Summary statistics include measures of central tendency like mean, median and mode, and measures of dispersion like range, variance and standard deviation.
This document provides an overview of different methods for displaying and describing data, including graphs, measures of center, and measures of spread. It discusses bar graphs, pie charts, stem-and-leaf plots, histograms, and calculating the mean, median, quartiles, interquartile range, and standard deviation. Examples are provided using data on football scores to demonstrate these concepts. Key terms like outliers, shape, and transformations of data are also introduced.
This document provides an overview of descriptive statistics techniques including frequencies, percentages, measures of central tendency, dispersion, correlations, and reliability. It describes graphical and tabular ways to present categorical and continuous data, such as bar charts, histograms, scatterplots and crosstabulations. It also defines key concepts like mean, median, standard deviation, range, correlation, and reliability coefficients. Multiple examples are provided to illustrate concepts like correlation, standard deviation, and how to interpret reliability.
Statistical Analysis is complex part but reporting of data in proper manner with proper selective graphs & interpretations is also necessary part of data analysis !!!
This document provides an outline and overview of descriptive statistics. It discusses the key concepts including:
- Visualizing and understanding data through graphs and charts
- Measures of central tendency like mean, median, and mode
- Measures of spread like range, standard deviation, and interquartile range
- Different types of distributions like symmetrical, skewed, and their properties
- Levels of measurement for variables and appropriate statistics for each level
The document serves as an introduction to descriptive statistics, the goals of which are to summarize key characteristics of data through numerical and visual methods.
This document provides information on frequency tables and statistical concepts. It defines key terms like population, sample, qualitative and quantitative variables. It also explains how to create frequency tables for qualitative and quantitative variables by listing the values/intervals, frequencies, relative frequencies, and cumulative relative frequencies. Examples of frequency tables are provided for different types of data. The document is intended to teach students about organizing and representing statistical data in tables.
Fuzzy similarity measure for classification.pptxsahayakagri
This document discusses fuzzy similarity measures which measure how close fuzzy sets are to each other. It provides an example calculating similarity scores between expert fuzzy diagnosis sets and a patient's symptoms set. The highest similarity score of 0.48 is between the patient's set and the "Chest Problem" expert set, correctly diagnosing the patient's condition. Fuzzy similarity measures are useful in data science for measuring how related data samples are.
The document contains 8 word problems involving percentages. The problems cover topics such as finding percentages, ratios, increases/decreases in quantities, and solving for unknown values. The word problems are presented along with multiple choice answer options.
RUNNING HEAD Descriptive Statistics and Interpretation 2.docxcharisellington63520
RUNNING HEAD: Descriptive Statistics and Interpretation 2
Oliver Jackson Iv
DESCRIPTIVE STATISTICS AND INTERPRETATION
QNT/561
9/8/2014
Joseph Hamel
Descriptive Statistics
Sales (in USD)
The distribution is normally distributed.
Central Tendency:
Mean = 42.84 dollars.
Dispersion:
Standard deviation = 9.073 dollars.
Count:
100
Min/Max:
Min is $23.00; Max is $64.00
Confidence Interval (alpha = 0.05):
$41.06 to $44.62
The histogram is present in Appendix A; the descriptive statistics are present in Appendix B.
Age
The distribution is not normally distributed.
Central Tendency:
Median = 35 years
Dispersion:
Interquartile Range = 12 years / 2 = ± 6 years
Count:
100
Min/Max:
Min is 25 years; Max is 45 years
Confidence Interval:
The data is not normally distributed, therefore there is no confidence interval
The histogram is present in Appendix A; the descriptive statistics are present in Appendix B; the scatterplot relating age and sales is in Appendix C.
ID On Display
Thirty-four percent of the people sampled did not have their ID on display while sixty-six percent of people sampled had their ID on display. The bar chart is in Appendix E.
Descriptive Statistics Interpretation
Sales
One hundred people were randomly selected, and their sales were measured. Their sales were observed between $23.00 and $64.00. The average sales were $42.84, with a standard deviation of $9.07. Approximately half or more of the data values are above $42.84. There is enough evidence to say that the population sales amount lies between $41.06 and $44.62 with 95% confidence.
Age
The data is not normally distributed and is skewed to the left. One hundred people were randomly selected, and their ages were measured. Their ages were between 25 and 45 years, with a variation of ± 6 years. Half of the people’s ages lie above 35 years. The middle half of the peoples’ ages are between 28 and 40 years. The mode, or the most frequent age, was 25 years.
APPENDIX A
Sales and Age Histograms
APPENDIX B
Descriptive Statistics on Sales and Age
APPENDIX C
Scatterplot of Age versus Sales
APPENDIX D
Sales vs. Ages Regression Information
APPENDIX E
Bar chart for ID on Display
Bin
Frequency
0
34
1
66
More
0
Key:
0-1 represents 0, or not on display
1-2 represents 1, or on display
Sales vs. Age
35.0 26.0 31.0 42.0 41.0 26.0 25.0 33.0 40.0 42.0 37.0 28.0 28.0 25.0 36.0 41.0 45.0 42.0 26.0 33.0 35.0 41.0 36.0 41.0 26.0 29.0 30.0 41.0 42.0 39.0 28.0 25.0 45.0 32.0 38.0 26.0 29.0 41.0 25.0 28.0 45.0 35.0 39.0 37.0 29.0 28.0 42.0 26.0 37.0 35.0 28.0 26.0 29.0 30.0 35.0 39.0 45.0 42.0 43.0 38.0 37.0 25.0 25.0 36.0 38.0 45.0 41.0 40.0 40.0 30.0 30.0 32.0 37.0 26.0 28.0 29.0 25.0 32.0 26.0 25.0 41.0 45.0 28.0 27.0 32.0 30.0 40.0 40.0 36.0 37.0 25.0 28.0 40.0 43.0 36.0 25.0 29.0 25.0 30.0 40.0 44.0 53.0 56.0 26.0 43.0 53.0 43.0 31.0 43.0 36.0 39..
This document provides information on different types of charts and graphs used in statistics. It defines bar graphs, pie charts, histograms, frequency polygons, ogives, pictograms and discusses their uses, advantages and disadvantages. Examples are given for each type of graph to demonstrate how they are constructed and how data is represented visually. Key information on choosing appropriate scales and plotting points for different graphs is also presented.
Understanding statistics 2#4 Using a Z Chart TableFlorin Neagu
The z-table is short for the “Standard Normal z-table”. The Standard Normal model is used in hypothesis testing, including tests on proportions and on the difference between two means. The area under the whole of a normal distribution curve is 1, or 100 percent.
Understanding Statistics 2#3 The Standard Normal DistributionFlorin Neagu
The standard normal distribution is a special case of the normal distribution. It is the distribution that occurs when a normal random variable has a mean of zero and a standard deviation of one.
The normal random variable of a standard normal distribution is called a standard score or a z score. Every normal random variable X can be transformed into a z score
More Related Content
Similar to Understanding statistics 1#4 Pie chart, Bar chart, Pareto,Histogram, Steam Leafs
1) The document contains data on learning achievement test scores from 80 students. It includes the frequency table, histogram, and polygon frequency diagram. It also calculates the mean score of 77.
2) A second dataset is provided with test scores and frequencies totaling 80 students. It calculates the mean score of 77, draws the ogive diagram, and estimates the median score as 77.3.
3) Monthly ticket sales data from 2018 is presented for 12 months totaling 1550 thousand tickets. It converts the sales to percentages and draws a pie chart comparing the monthly sales.
This document provides an overview of different types of variables and methods for summarizing clinical data, including descriptive statistics. It discusses categorical variables like gender and ordinal variables like disease staging. For continuous variables it explains measures of central tendency like mean, median and mode, and measures of variation like range, standard deviation, and interquartile range. Graphs for summarizing univariate data are also covered, such as bar charts for categorical variables and histograms and box plots for continuous variables.
The document provides admissions statistics for Nazarbayev University Foundation Year Programme (NUFYP) from 2010 to 2017. It summarizes the number of applicants, exam takers who met admission criteria, and students accepted each year. It also breaks down admission numbers and test scores by gender, region of Kazakhstan, and NU school. The number of applicants and exam takers generally decreased from 2010 to 2017 while the number accepted fluctuated between 484 and 706.
The document provides admissions statistics for Nazarbayev University Foundation Year Programme (NUFYP) from 2010 to 2017. It summarizes the number of applicants, exam takers who met admission criteria, and students accepted each year. It also breaks down admission numbers and test scores by subject, region, gender, and NU school. The number of applicants and exam takers generally decreased from 2010 to 2017 while the number accepted fluctuated between 484 and 706. Engineering was the most popular school for admitted students throughout this period.
When fitting loss data (insurance) to a distribution, often the parameters that provide a good overall fit will understate the density in the tail.
This method allows one to split the distribution into 2 portions, and use a Pareto distribution to fit the tail.
Presented at the CAS Spring Meeting in Seattle, May 2016.
This document describes Joseph Reiter's analysis of unemployment data from the 2010 Census and 2008-2012 American Community Survey. It discusses: 1) reducing 559 variables down to 58 then selecting a 5-variable model to predict unemployment, 2) comparing models with different transformations of the response variable, 3) evaluating residuals and normality, and 4) concluding the selected model is robust but regional effects were less significant than expected and causation cannot be determined.
This document provides information on percentages, sequences, shapes, mean, median, mode, and range. It includes examples and questions for finding percentages by multiplication, percentage increase and decrease calculations, and formulas for calculating the areas of different shapes including rectangular prisms, triangular prisms, triangles, parallelograms, and trapezoids. It also mentions mean, median, mode, and range but does not provide details on these topics.
The document appears to be a survey containing respondents' answers to questions about experiences with unidentified flying objects (UFOs) and other paranormal phenomena. Some key details:
- 76.22% of respondents reported seeing an intelligently controlled craft in the sky or ground not made by humans.
- 60.40% of those who saw a UFO said that other witnesses also saw it.
- 41.64% believe they possess knowledge about advanced technology or physics they have not learned.
- 80.52% reported experiencing altered states like out of body experiences.
- 56.61% believe they have observed a non-human intelligent entity.
So in summary, the
This document provides an introduction to biostatistics. It defines biostatistics as the application of statistics to biology. Biostatistics involves collecting, presenting, analyzing, and interpreting biological data to make decisions. It also discusses different types of data, methods for presenting data including tables, graphs, and summary statistics. Common graphical methods include histograms, bar charts, and pie charts. Summary statistics include measures of central tendency like mean, median and mode, and measures of dispersion like range, variance and standard deviation.
This document provides an overview of different methods for displaying and describing data, including graphs, measures of center, and measures of spread. It discusses bar graphs, pie charts, stem-and-leaf plots, histograms, and calculating the mean, median, quartiles, interquartile range, and standard deviation. Examples are provided using data on football scores to demonstrate these concepts. Key terms like outliers, shape, and transformations of data are also introduced.
This document provides an overview of descriptive statistics techniques including frequencies, percentages, measures of central tendency, dispersion, correlations, and reliability. It describes graphical and tabular ways to present categorical and continuous data, such as bar charts, histograms, scatterplots and crosstabulations. It also defines key concepts like mean, median, standard deviation, range, correlation, and reliability coefficients. Multiple examples are provided to illustrate concepts like correlation, standard deviation, and how to interpret reliability.
Statistical Analysis is complex part but reporting of data in proper manner with proper selective graphs & interpretations is also necessary part of data analysis !!!
This document provides an outline and overview of descriptive statistics. It discusses the key concepts including:
- Visualizing and understanding data through graphs and charts
- Measures of central tendency like mean, median, and mode
- Measures of spread like range, standard deviation, and interquartile range
- Different types of distributions like symmetrical, skewed, and their properties
- Levels of measurement for variables and appropriate statistics for each level
The document serves as an introduction to descriptive statistics, the goals of which are to summarize key characteristics of data through numerical and visual methods.
This document provides information on frequency tables and statistical concepts. It defines key terms like population, sample, qualitative and quantitative variables. It also explains how to create frequency tables for qualitative and quantitative variables by listing the values/intervals, frequencies, relative frequencies, and cumulative relative frequencies. Examples of frequency tables are provided for different types of data. The document is intended to teach students about organizing and representing statistical data in tables.
Fuzzy similarity measure for classification.pptxsahayakagri
This document discusses fuzzy similarity measures which measure how close fuzzy sets are to each other. It provides an example calculating similarity scores between expert fuzzy diagnosis sets and a patient's symptoms set. The highest similarity score of 0.48 is between the patient's set and the "Chest Problem" expert set, correctly diagnosing the patient's condition. Fuzzy similarity measures are useful in data science for measuring how related data samples are.
The document contains 8 word problems involving percentages. The problems cover topics such as finding percentages, ratios, increases/decreases in quantities, and solving for unknown values. The word problems are presented along with multiple choice answer options.
RUNNING HEAD Descriptive Statistics and Interpretation 2.docxcharisellington63520
RUNNING HEAD: Descriptive Statistics and Interpretation 2
Oliver Jackson Iv
DESCRIPTIVE STATISTICS AND INTERPRETATION
QNT/561
9/8/2014
Joseph Hamel
Descriptive Statistics
Sales (in USD)
The distribution is normally distributed.
Central Tendency:
Mean = 42.84 dollars.
Dispersion:
Standard deviation = 9.073 dollars.
Count:
100
Min/Max:
Min is $23.00; Max is $64.00
Confidence Interval (alpha = 0.05):
$41.06 to $44.62
The histogram is present in Appendix A; the descriptive statistics are present in Appendix B.
Age
The distribution is not normally distributed.
Central Tendency:
Median = 35 years
Dispersion:
Interquartile Range = 12 years / 2 = ± 6 years
Count:
100
Min/Max:
Min is 25 years; Max is 45 years
Confidence Interval:
The data is not normally distributed, therefore there is no confidence interval
The histogram is present in Appendix A; the descriptive statistics are present in Appendix B; the scatterplot relating age and sales is in Appendix C.
ID On Display
Thirty-four percent of the people sampled did not have their ID on display while sixty-six percent of people sampled had their ID on display. The bar chart is in Appendix E.
Descriptive Statistics Interpretation
Sales
One hundred people were randomly selected, and their sales were measured. Their sales were observed between $23.00 and $64.00. The average sales were $42.84, with a standard deviation of $9.07. Approximately half or more of the data values are above $42.84. There is enough evidence to say that the population sales amount lies between $41.06 and $44.62 with 95% confidence.
Age
The data is not normally distributed and is skewed to the left. One hundred people were randomly selected, and their ages were measured. Their ages were between 25 and 45 years, with a variation of ± 6 years. Half of the people’s ages lie above 35 years. The middle half of the peoples’ ages are between 28 and 40 years. The mode, or the most frequent age, was 25 years.
APPENDIX A
Sales and Age Histograms
APPENDIX B
Descriptive Statistics on Sales and Age
APPENDIX C
Scatterplot of Age versus Sales
APPENDIX D
Sales vs. Ages Regression Information
APPENDIX E
Bar chart for ID on Display
Bin
Frequency
0
34
1
66
More
0
Key:
0-1 represents 0, or not on display
1-2 represents 1, or on display
Sales vs. Age
35.0 26.0 31.0 42.0 41.0 26.0 25.0 33.0 40.0 42.0 37.0 28.0 28.0 25.0 36.0 41.0 45.0 42.0 26.0 33.0 35.0 41.0 36.0 41.0 26.0 29.0 30.0 41.0 42.0 39.0 28.0 25.0 45.0 32.0 38.0 26.0 29.0 41.0 25.0 28.0 45.0 35.0 39.0 37.0 29.0 28.0 42.0 26.0 37.0 35.0 28.0 26.0 29.0 30.0 35.0 39.0 45.0 42.0 43.0 38.0 37.0 25.0 25.0 36.0 38.0 45.0 41.0 40.0 40.0 30.0 30.0 32.0 37.0 26.0 28.0 29.0 25.0 32.0 26.0 25.0 41.0 45.0 28.0 27.0 32.0 30.0 40.0 40.0 36.0 37.0 25.0 28.0 40.0 43.0 36.0 25.0 29.0 25.0 30.0 40.0 44.0 53.0 56.0 26.0 43.0 53.0 43.0 31.0 43.0 36.0 39..
This document provides information on different types of charts and graphs used in statistics. It defines bar graphs, pie charts, histograms, frequency polygons, ogives, pictograms and discusses their uses, advantages and disadvantages. Examples are given for each type of graph to demonstrate how they are constructed and how data is represented visually. Key information on choosing appropriate scales and plotting points for different graphs is also presented.
Similar to Understanding statistics 1#4 Pie chart, Bar chart, Pareto,Histogram, Steam Leafs (20)
Understanding statistics 2#4 Using a Z Chart TableFlorin Neagu
The z-table is short for the “Standard Normal z-table”. The Standard Normal model is used in hypothesis testing, including tests on proportions and on the difference between two means. The area under the whole of a normal distribution curve is 1, or 100 percent.
Understanding Statistics 2#3 The Standard Normal DistributionFlorin Neagu
The standard normal distribution is a special case of the normal distribution. It is the distribution that occurs when a normal random variable has a mean of zero and a standard deviation of one.
The normal random variable of a standard normal distribution is called a standard score or a z score. Every normal random variable X can be transformed into a z score
Understanding Statistics 2#2 The Normal Probability DistributionFlorin Neagu
The normal distribution is the most important and most widely used distribution in statistics.
It is sometimes called the "bell curve," Strictly speaking, it is not correct to talk about "the normal distribution" since there are many normal distributions. Normal distributions can differ in their means and in their standard deviations. Normal distributions are symmetric with relatively more values at the center of the distribution and relatively few in the tails.
Understanding statistics 2#1 Random Variation and Discrete Probability Distri...Florin Neagu
Random Variable
In algebra, you learned about different variables like x, y and maybe even z. Some examples of variables include x = number of heads or y = number of cell phones or z = running time of movies. Thus, in basic math, a variable is an alphabetical character that represents an unknown number.
In probability, we also have variables and we refer to them as random variables. A random variable is a variable that is subject to randomness, which means it can take on different values. For example, tossing a coin, cast a dice, etc.
The random variable takes on different values depending on the situation. Each value of the random variable has a probability or percentage associated with it
Discrete Probability Distribution
A discrete distribution describes the probability of occurrence of each value of a discrete random variable. With a discrete probability distribution, each possible value of the discrete random variable can be associated with a non-zero probability
Z-score is a numerical measurement of a value's relationship to the mean in a group of values. If a Z-score is 0, then the score is identical to the mean score.
Z-scores can be positive or negative, with a positive value indicating the score is above the mean and a negative score indicating it is below the mean.
Z-score or the standard score is a very useful statistic because it
- allows us to calculate the probability of a score occurring within our normal distribution
- enables us to compare two scores that are from different normal distributions.
Understanding Statistics 1#13 Box and Whisker plotsFlorin Neagu
A box and whisker plot (boxplot) is a graph that presents information from a five-number summary. It is especially useful for indicating whether a distribution is skewed and whether there are potential unusual observations (outliers) in the data set. Box and whisker plots are also very useful when large numbers of observations are involved and when two or more data sets are being compared
A box and whisker plot is a way of summarizing a set of data measured on an interval scale. It is often used in explanatory data analysis. This type of graph is used to show the shape of the distribution, its central value, and its variability.
Understanding Statistics 1#12 Quartiles of DataFlorin Neagu
To understand a quartile, let us revisit median. For median, we cut off the data into two groups with equal number of points. Thus the middle value that separates these groups is the median. In the same way if we divide the data into 4 equal groups, the first differentiating point is the first quartile, the second differentiating point is the second quartile (which is the same as the median) and the third differentiating point is the third quartile.
To further see what quartiles do, the first quartile is at the 25th percentile. This means that 25% of the data is smaller than the first quartile and 75% of the data is larger than this. Similarly, in case of the third quartile, 25% of the data is larger than it while 75% of it is smaller. For the second quartile, which is nothing but the median, 50% or half of the data is smaller while half of the data is larger than this value
The Chebyshev theorem provides a lower bound for the percentage of data that lies within a certain number of standard deviations from the mean, even if the data is not normally distributed. It states that at least 75% of data lies within 2 standard deviations of the mean, and at least 88.9% lies within 3 standard deviations of the mean. The document provides examples of applying the Chebyshev theorem to calculate the minimum percentage of homes with incomes and water bills within given ranges.
Master statistics 1#10 Empirical Rule of Standard Deviation Florin Neagu
The empirical rule is a statistical rule which declares that for a normal distribution, almost all data will fall within three standard deviations of the mean.
The empirical rule is most often used in statistics to anticipate final outcomes. After a standard deviation is calculated and before exact data can be collected, this rule can be used as a rough estimate of the outcome of the data. This probability can be used meanwhile since gathering appropriate data may be time-consuming or even impossible to obtain. The empirical rule is also used as a rough way to test a distribution's "normality". If too many data points fall outside the three standard deviation boundaries, this could suggest that the distribution is not normal
Master statistics 1#09_ Standard Deviation of Data in a Frequency TableFlorin Neagu
When using standard deviation keep in mind the following properties.
• Standard deviation is only used to measure spread or dispersion around the mean of a data set.
• Standard deviation is never negative.
• Standard deviation is sensitive to outliers. A single outlier can raise the standard deviation and in turn, distort the picture of spread.
• For data with approximately the same mean, the greater the spread, the greater the standard deviation.
Master statistics 1#8 Population and Sample Standard DeviationFlorin Neagu
- The standard deviation is the square root of the variance. The standard deviation is expressed in the same units as the mean is, whereas the variance is expressed in squared units.
- Both standard deviation and variance are derived from the mean of a given data set.
- The coefficient of variation is a standardized measure of dispersion of a probability distribution or frequency distribution. If you know nothing about the data other than the mean, one way to interpret the relative magnitude of the standard deviation is to divide it by the mean
Master statistics 1#7 Population and Sample VarianceFlorin Neagu
• Variance: this is a very important concept in statistics and represents how spread the data is
Variance is used to see how individual numbers relate to each other within a data set.
- A drawback to variance is that it gives added weight to numbers far from the mean (outliers), since squaring these numbers can skew interpretations of the data.
- The advantage of variance is that it treats all deviations from the mean the same regardless of direction; as a result, the squared deviations cannot sum to zero and give the appearance of no variability.
- The drawback of variance is that it is not easily interpreted, and the square root of its value is usually taken to get the standard deviation of the data set in question.
Master statistics 1#6 Median vs. Mean and Range of DataFlorin Neagu
• Median: the middle value in an ordered list of data
• If an odd number of sample then chose the median as the middle
• If even number then the median is average of the 2 middle sample
• Mode: value in the dataset that occur most frequently
• Mean: Average value of the data
• Measure of dispersion: how much data is spread or dispersed
• Range of the data: Difference between largest and smallest values
Understanding statistics 1#5 Mean and Sample MeanFlorin Neagu
Mean: Average value of the data
Sample Mean: Mean of the sample values collected;
Weighted mean: when each value of the dataset is not equally important
Master statistics 1#3 _relative and cumulative frequency distributionFlorin Neagu
The document discusses relative and cumulative frequency distributions. Relative frequency is calculated as the class frequency divided by the total frequency and expresses the percentage of the data that falls into each class. Cumulative frequency is the sum of a class frequency and all previous class frequencies and represents the running total. An example of calculating relative and cumulative frequencies is provided for age data on when people bought their first car and got their first job.
Frequency Distribution
In statistics, a frequency distribution is a list, table or graph that displays the frequency of various outcomes in a sample.
Each entry in the table contains the frequency or count of the occurrences of values within a particular group or interval, and in this way, the table summarizes the distribution of values in the sample.
A brief description of how to "handle" any type of data you come across.
This cover:
- Type of data,
- Data representation; or how to visualize data to understand relationship
- Which control chart should I use to check the process capability
Testing common assumptions; which hypothesis should be used it and how to check
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Discover the cutting-edge telemetry solution implemented for Alan Wake 2 by Remedy Entertainment in collaboration with AWS. This comprehensive presentation dives into our objectives, detailing how we utilized advanced analytics to drive gameplay improvements and player engagement.
Key highlights include:
Primary Goals: Implementing gameplay and technical telemetry to capture detailed player behavior and game performance data, fostering data-driven decision-making.
Tech Stack: Leveraging AWS services such as EKS for hosting, WAF for security, Karpenter for instance optimization, S3 for data storage, and OpenTelemetry Collector for data collection. EventBridge and Lambda were used for data compression, while Glue ETL and Athena facilitated data transformation and preparation.
Data Utilization: Transforming raw data into actionable insights with technologies like Glue ETL (PySpark scripts), Glue Crawler, and Athena, culminating in detailed visualizations with Tableau.
Achievements: Successfully managing 700 million to 1 billion events per month at a cost-effective rate, with significant savings compared to commercial solutions. This approach has enabled simplified scaling and substantial improvements in game design, reducing player churn through targeted adjustments.
Community Engagement: Enhanced ability to engage with player communities by leveraging precise data insights, despite having a small community management team.
This presentation is an invaluable resource for professionals in game development, data analytics, and cloud computing, offering insights into how telemetry and analytics can revolutionize player experience and game performance optimization.
Do People Really Know Their Fertility Intentions? Correspondence between Sel...Xiao Xu
Fertility intention data from surveys often serve as a crucial component in modeling fertility behaviors. Yet, the persistent gap between stated intentions and actual fertility decisions, coupled with the prevalence of uncertain responses, has cast doubt on the overall utility of intentions and sparked controversies about their nature. In this study, we use survey data from a representative sample of Dutch women. With the help of open-ended questions (OEQs) on fertility and Natural Language Processing (NLP) methods, we are able to conduct an in-depth analysis of fertility narratives. Specifically, we annotate the (expert) perceived fertility intentions of respondents and compare them to their self-reported intentions from the survey. Through this analysis, we aim to reveal the disparities between self-reported intentions and the narratives. Furthermore, by applying neural topic modeling methods, we could uncover which topics and characteristics are more prevalent among respondents who exhibit a significant discrepancy between their stated intentions and their probable future behavior, as reflected in their narratives.
Optimizing Feldera: Integrating Advanced UDFs and Enhanced SQL Functionality ...mparmparousiskostas
This report explores our contributions to the Feldera Continuous Analytics Platform, aimed at enhancing its real-time data processing capabilities. Our primary advancements include the integration of advanced User-Defined Functions (UDFs) and the enhancement of SQL functionality. Specifically, we introduced Rust-based UDFs for high-performance data transformations and extended SQL to support inline table queries and aggregate functions within INSERT INTO statements. These developments significantly improve Feldera’s ability to handle complex data manipulations and transformations, making it a more versatile and powerful tool for real-time analytics. Through these enhancements, Feldera is now better equipped to support sophisticated continuous data processing needs, enabling users to execute complex analytics with greater efficiency and flexibility.
2. Pie chart, Bar graph and Pareto charts
Pie Chart: Data represented as a fraction of a circle
Find wedges:
Yellow = 0.1 (360) = 36◦
Blue = 0.41 * 360 = 147.6◦
Purple = 0.18 * 360 = 64.8◦
Red = 0.31*360 = 111.6◦
Favorite Color of Males
yellow purple red blue
colour percentage
Yellow 10
Purple 18
Red 31
Blue 41
3. Pie chart, Bar graph and Pareto charts
Pie Chart: Data represented as a fraction of a circle
Find wedges:
Yellow = 0.1 (360) = 36◦
Blue = 0.41 * 360 = 147.6◦
Purple = 0.18 * 360 = 64.8◦
Red = 0.31*360 = 111.6◦
Bar Graph:
Bars do not touch (have a gap)
With of bar are equal
Pareto chart: same as bar chart in descending order
Favorite Color of Males
yellow purple red blue
colour percentage
Yellow 10%
Purple 18%
Red 31%
Blue 41%
0%
20%
40%
60%
Yellow Purple Red Blue
percentage
percentage
4. Histograms
Histogram: Bar chart of a frequency distribution
Height of trees in a field
Bars are united, do touch
Boundaries is shared between two adjacent
Relative Frequency Histogram: heights show rel. freq.
Age of kids that learn to walk
Breaking time for cars in minutes
For this you need to calculate and use
the class boundaries,
note the class don’t overlap
Height Frequency
60.01 - 65 3
65.01 - 70 4
70.01 - 75 8
75.01 - 80 5
80.01 - 85 3
85.01 - 90 1
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
60.00 65.00 70.00 75.00 80.00 85.00
Frequency
Height (feet)
Heights of trees in a field
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
6.00 8.00 10.00 12.00
Percentage
Months
Age of Kids learn to walk
Class Frequency Class Boundaries
0.05 – 0.07 12 0.045 – 0.075
0.08 – 0.10 15 0.075 – 0.105
0.11 – 0.13 14 0.105 – 0.135
0.14 – 0.16 15 0.135 – 0.165
0.17 – 0.19 14 0.165 – 0.195
0
2
4
6
8
10
12
14
16
0.045 –
0.075
0.075 –
0.105
0.105 –
0.135
0.135 –
0.165
0.165 –
0.195
No.Cars
Minutes
Breaking time for cars
5. Steam and Leaf Plots
ACT Scores:
Stem: is the rest without leaves
Leaves: last digit of the data
Can order if needed
18 23 24 31 19
27 26 22 32 18
25 27 27 29 20
18 17 21 25 26
Stem Leaves
1 8 9 8 8 7
2 3 4 7 6 2 5 7 7 9 0 1 5
3 1 2
Stem Leaves
1 7 8 8 8 9
2 0 1 1 2 3 4 5 5 6 7 7 9
3 1 2
Stem Leaves
1
1 7 8 8 8 9
2 0 1 1 2 3 4 5 5
2 6 7 7 9
3 1 2
3
split the leaves interval in 2: 0-5 & 6-9
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