Descriptive statistics and Regression
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Statistical Prediction for analyzing Epidemiological Characteristics of COVID...Nuwan Sriyantha Bandara
In this presentation, we propose a novel integrated model for analyzing the characteristics of the epidemiological curve of COVID-19 by utilizing an enhanced compartmental statistical prediction model which is developed conferring susceptible-infectious susceptible (SIS) model, susceptible-infectious-removed (SIR) model, Dirichlet process model, and the interpretive structural model.
The oral presentation of the research has been presented at the International Research Conference 2020 of Sri Lanka Technological Campus on 17th June, 2020.
Abstract Link: http://repo.sltc.ac.lk/handle/1/82
This document outlines the course syllabus for Statistical Methods 1 taught by Mikol A. Mortley at the University of the West Indies, Mona Campus Department of Economics. The course covers introductory statistical topics including numerical summaries, graphical descriptions of data, random variables and probability distributions, interval estimation, sampling and sampling distributions, and hypothesis testing. Specific statistical measures, distributions, and tests that will be covered include means, medians, variance, the normal, binomial, and exponential distributions, interval estimation for proportions, means, and standard deviations, and hypothesis testing for means, proportions, differences between means and proportions, and chi-square goodness of fit tests.
This document provides an introduction and table of contents to the book "Advanced Sampling Theory with Applications" by Sarjinder Singh. It discusses sampling concepts such as populations, samples, parameters, statistics, probability sampling, and properties of estimators. It also outlines the topics that will be covered in each chapter, including simple random sampling, use of auxiliary information, probability proportional to size sampling, and estimation techniques.
The document discusses various measures of statistical variation that can be used to analyze and describe the spread or dispersion of data values in a data set. It defines and provides examples to calculate and interpret range, standard deviation, variance, interquartile range, and coefficient of variation. It also discusses box plots and how they can be used as a graphical method to visualize the five number summary of a data set. Microsoft Excel functions like STDEV and descriptive statistics tools are demonstrated for computing some of these measures of variation from a data set.
Analytics is the process of examining data to draw conclusions and inform decision making. It involves descriptive, predictive, and prescriptive models. Descriptive models analyze past data to understand what has occurred, predictive models use statistical techniques to forecast future outcomes, and prescriptive models advise on potential actions and outcomes. Common techniques in analytics include statistics, machine learning, and visualization of large datasets.
This document defines key statistical concepts including mean, median, mode, and standard deviation. It explains how to calculate the mean by summing all values and dividing by the total number of values. The median is the middle value in a data set. The mode is the most frequently occurring value. Standard deviation is calculated by finding the mean, deviations from the mean, squaring the deviations, taking the average of the squared deviations (variance), and then calculating the square root of the variance. An example calculation of standard deviation for the data set {1,4,5,6,9} is provided. The document also lists 10 problems for further practice.
Imputation techniques for missing data in clinical trialsNitin George
Missing data are unavoidable in clinical and epidemiological researches. Missing data leads to bias and loss of information in research analysis. Usually we are not aware of missing data techniques because we are depending on some software’s. The objective of this seminar is to introduce different missing data mechanisms and imputation techniques for missing data with the help of examples.
Statistical Prediction for analyzing Epidemiological Characteristics of COVID...Nuwan Sriyantha Bandara
In this presentation, we propose a novel integrated model for analyzing the characteristics of the epidemiological curve of COVID-19 by utilizing an enhanced compartmental statistical prediction model which is developed conferring susceptible-infectious susceptible (SIS) model, susceptible-infectious-removed (SIR) model, Dirichlet process model, and the interpretive structural model.
The oral presentation of the research has been presented at the International Research Conference 2020 of Sri Lanka Technological Campus on 17th June, 2020.
Abstract Link: http://repo.sltc.ac.lk/handle/1/82
This document outlines the course syllabus for Statistical Methods 1 taught by Mikol A. Mortley at the University of the West Indies, Mona Campus Department of Economics. The course covers introductory statistical topics including numerical summaries, graphical descriptions of data, random variables and probability distributions, interval estimation, sampling and sampling distributions, and hypothesis testing. Specific statistical measures, distributions, and tests that will be covered include means, medians, variance, the normal, binomial, and exponential distributions, interval estimation for proportions, means, and standard deviations, and hypothesis testing for means, proportions, differences between means and proportions, and chi-square goodness of fit tests.
This document provides an introduction and table of contents to the book "Advanced Sampling Theory with Applications" by Sarjinder Singh. It discusses sampling concepts such as populations, samples, parameters, statistics, probability sampling, and properties of estimators. It also outlines the topics that will be covered in each chapter, including simple random sampling, use of auxiliary information, probability proportional to size sampling, and estimation techniques.
The document discusses various measures of statistical variation that can be used to analyze and describe the spread or dispersion of data values in a data set. It defines and provides examples to calculate and interpret range, standard deviation, variance, interquartile range, and coefficient of variation. It also discusses box plots and how they can be used as a graphical method to visualize the five number summary of a data set. Microsoft Excel functions like STDEV and descriptive statistics tools are demonstrated for computing some of these measures of variation from a data set.
Analytics is the process of examining data to draw conclusions and inform decision making. It involves descriptive, predictive, and prescriptive models. Descriptive models analyze past data to understand what has occurred, predictive models use statistical techniques to forecast future outcomes, and prescriptive models advise on potential actions and outcomes. Common techniques in analytics include statistics, machine learning, and visualization of large datasets.
This document defines key statistical concepts including mean, median, mode, and standard deviation. It explains how to calculate the mean by summing all values and dividing by the total number of values. The median is the middle value in a data set. The mode is the most frequently occurring value. Standard deviation is calculated by finding the mean, deviations from the mean, squaring the deviations, taking the average of the squared deviations (variance), and then calculating the square root of the variance. An example calculation of standard deviation for the data set {1,4,5,6,9} is provided. The document also lists 10 problems for further practice.
Imputation techniques for missing data in clinical trialsNitin George
Missing data are unavoidable in clinical and epidemiological researches. Missing data leads to bias and loss of information in research analysis. Usually we are not aware of missing data techniques because we are depending on some software’s. The objective of this seminar is to introduce different missing data mechanisms and imputation techniques for missing data with the help of examples.
This document discusses statistics and its importance in research. It explains that statistics is used to make wise decisions in the presence of uncertainty by collecting, analyzing, and interpreting data. Some key areas that use statistics include drug development, quality control, and pharmaceutical research. The document outlines the basic steps in research, including designing experiments, collecting data, analyzing results, and drawing conclusions. It also discusses important statistical concepts such as variables, probability, confidence intervals, and choosing the appropriate statistical test.
OER Descriptive Statistics (University of Edinburgh)LeonardHo7
This Open Educational Resource (OER) introduces the concepts of quantitative and qualitative statistics, central tendency (mean, median, and mode), and dispersion (standard deviation and interquartile range). This document is created for PgCAP Digital Education.
data analysis in Statistics-2023 guide 2023ayesha455941
- Statistics is the science of collecting, analyzing, interpreting, presenting, and organizing data. It is used across various fields including physics, business, social sciences, and healthcare.
- There are two main branches of statistical analysis: descriptive statistics, which summarizes and describes data, and inferential statistics, which draws conclusions about populations based on samples.
- Key concepts include populations, samples, parameters, statistics, and the differences between descriptive and inferential analysis. Measures of central tendency like the mean, median, and mode are used to describe data, while measures of variation like the range, variance, and standard deviation quantify how spread out the data is.
This document provides an introduction to biostatistics and descriptive statistics concepts. It defines key terms like data, variables, populations, samples, and measurement scales. It also discusses measures of central tendency like mean, median and mode. Measures of dispersion such as range, variance, standard deviation and coefficient of variation are introduced. Finally, the document discusses frequency distributions, histograms, percentiles, quartiles, and box plots as ways to summarize and visualize data distributions. Examples are provided throughout to illustrate statistical concepts.
The document discusses measures of variability in statistics including range, interquartile range, standard deviation, and variance. It provides examples of calculating each measure using sample data sets. The range is the difference between the highest and lowest values, while the interquartile range is the difference between the third and first quartiles. The standard deviation represents the average amount of dispersion from the mean, and variance is the average of the squared deviations from the mean. Both standard deviation and variance increase with greater variability in the data set.
This document provides an overview of statistical tests of significance. It introduces key concepts like data types, measures of central tendency and dispersion, hypotheses, errors, power and level of significance. It discusses parametric tests like t-tests, ANOVA, and correlation coefficients that require normal distribution of data. It also mentions non-parametric tests for non-normal data. The document provides examples and explanations to help understand these important statistical concepts and methods.
Statistics for the Health Scientist: Basic Statistics IIDrLukeKane
This document provides an overview of descriptive statistics concepts including measures of central tendency (mode, median, mean), measures of spread (range, interquartile range, standard deviation), transformation of data to normal distributions, and definitions of prevalence and incidence. It discusses choosing the appropriate statistical measure based on the type of variable and provides examples to illustrate prevalence, incidence, and how to calculate them from population data.
This document outlines key concepts for analyzing qualitative and quantitative data. It discusses preparing data through editing, coding and inserting into a matrix. Graphical techniques like histograms, scatter plots and box plots are presented for depicting individual, comparative and relational data. Measures of central tendency, dispersion, relationships and models are explained including mean, median, standard deviation, correlation, and linear and non-linear models. The goal is for students to understand how to analyze data using appropriate statistical techniques and data visualization.
OER Descriptive Statistics (University of Edinburgh)LeonardHo7
This Open Educational Resource (OER) introduces the concepts of quantitative and qualitative statistics, central tendency (mean, median, and mode), and dispersion (standard deviation and interquartile range).
This document is created for PgCAP Digital Education.
Univariate, bivariate analysis, hypothesis testing, chi squarekongara
This document provides an introduction to data analysis. It discusses various topics related to measurement and types of data, including univariate and bivariate analysis. For univariate analysis, it describes descriptive statistics such as mean, median, mode, variance, and standard deviation. It also discusses data distributions and different measurement scales. For bivariate analysis, it introduces cross-tabulation and chi-square tests to examine relationships between two variables. Cross-tabulation allows looking at associations between variables through frequencies and percentages in tables, while chi-square can be used to test hypotheses about relationships and determine statistical significance.
This document provides an overview of statistical analysis methods for metabolomics data, including data pre-treatment, univariate analysis, and multivariate analysis. It discusses normalization techniques, student's t-tests, volcano plots, principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). The goal of metabolomics data analysis is biomarker discovery and disease diagnosis by identifying significant metabolic features associated with conditions.
This document provides an overview of descriptive statistics concepts including data, variables, observations, and types of data such as quantitative, categorical, cross-sectional, and time series data. It discusses frequency distributions for categorical and quantitative data including histograms. Measures of central tendency like the mean, median, and mode are covered. Measures of variability including range, variance, standard deviation, and coefficient of variation are also introduced. The document concludes with a discussion of percentiles and calculating percentiles from a data set.
This document provides an overview of how to use SPSS to conduct basic statistical analysis and present results. It outlines expectations for the workshop, including learning how to prepare an SPSS file, display and summarize data, and create graphical presentations. The document then covers key SPSS concepts like variables, data types, and examples. It also demonstrates how to perform descriptive statistics, frequency tables, crosstabs, measures of central tendency and dispersion. Finally, it discusses different methods of graphical presentation in SPSS like bar charts, histograms, box plots and more.
One of the most important, yet often overlooked, aspects of predictive modeling is the transformation of data to create model inputs, better known as feature engineering (FE). This talk will go into the theoretical background behind FE, showing how it leverages existing data to produce better modeling results. It will then detail some important FE techniques that should be in every data scientist’s tool kit.
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 discusses interpreting machine learning models and summarizes techniques for interpreting random forests. Random forests are considered "black boxes" due to their complexity but their predictions can be explained by decomposing them into mathematically exact feature contributions. Decision trees can also be interpreted by defining the prediction as a bias plus the contributions from each feature along the decision path. This operational view of decision trees can be extended to interpret random forest predictions despite their complexity.
This document discusses various numerical measures used to describe data, including measures of central tendency (mean, median, mode) and measures of dispersion (range, variance, standard deviation). It provides definitions and formulas for calculating these measures, along with examples of calculating the mean, median, mode, weighted mean, variance, standard deviation, and other concepts. The learning objectives cover explaining central tendency, computing various means, determining the median and mode, calculating the geometric mean, explaining and applying measures of dispersion, computing standard deviation, and concepts like Chebyshev's Theorem and the Empirical Rule.
This document discusses measures of dispersion and the normal distribution. It defines measures of dispersion as ways to quantify the variability in a data set beyond measures of central tendency like mean, median, and mode. The key measures discussed are range, quartile deviation, mean deviation, and standard deviation. It provides formulas and examples for calculating each measure. The document then explains the normal distribution as a theoretical probability distribution important in statistics. It outlines the characteristics of the normal curve and provides examples of using the normal distribution and calculating z-scores.
This document discusses descriptive statistics used to summarize continuous data, including measures of central tendency (mean, median, mode), measures of dispersion (standard deviation, variance, range, interquartile range), and the normal distribution curve. It provides definitions and examples of how to calculate each measure. The key measures are the mean as the average, the median as the middle value, and the mode as the most frequent value. Standard deviation and variance quantify how spread out values are from the mean. Choosing the appropriate central tendency and dispersion measures depends on whether the data distribution is normal/symmetrical or skewed.
Exploratory data analysis (EDA) involves analyzing datasets to discover patterns, trends, and relationships. EDA techniques include graphical methods like histograms, box plots, and scatter plots as well as calculating summary statistics. The goal of EDA is to better understand the data structure and relationships between variables through visual and numerical techniques without beginning with a specific hypothesis. EDA is used to generate hypotheses for further confirmatory analysis and to identify outliers, anomalies, and other unusual data characteristics. Lattice graphics and other plotting functions in R can be useful tools for EDA to visualize univariate and bivariate relationships in data.
Financial Performance Analysis of Conventional and Non – Conventional BanksAN_Rajin
Financial Performance Analysis of Conventional and Non – Conventional Banks.
Hope this will help you. Please don't forget to like/comment. Your appreciation will motivate me to make further more slide. Thanks in advance
This document discusses statistics and its importance in research. It explains that statistics is used to make wise decisions in the presence of uncertainty by collecting, analyzing, and interpreting data. Some key areas that use statistics include drug development, quality control, and pharmaceutical research. The document outlines the basic steps in research, including designing experiments, collecting data, analyzing results, and drawing conclusions. It also discusses important statistical concepts such as variables, probability, confidence intervals, and choosing the appropriate statistical test.
OER Descriptive Statistics (University of Edinburgh)LeonardHo7
This Open Educational Resource (OER) introduces the concepts of quantitative and qualitative statistics, central tendency (mean, median, and mode), and dispersion (standard deviation and interquartile range). This document is created for PgCAP Digital Education.
data analysis in Statistics-2023 guide 2023ayesha455941
- Statistics is the science of collecting, analyzing, interpreting, presenting, and organizing data. It is used across various fields including physics, business, social sciences, and healthcare.
- There are two main branches of statistical analysis: descriptive statistics, which summarizes and describes data, and inferential statistics, which draws conclusions about populations based on samples.
- Key concepts include populations, samples, parameters, statistics, and the differences between descriptive and inferential analysis. Measures of central tendency like the mean, median, and mode are used to describe data, while measures of variation like the range, variance, and standard deviation quantify how spread out the data is.
This document provides an introduction to biostatistics and descriptive statistics concepts. It defines key terms like data, variables, populations, samples, and measurement scales. It also discusses measures of central tendency like mean, median and mode. Measures of dispersion such as range, variance, standard deviation and coefficient of variation are introduced. Finally, the document discusses frequency distributions, histograms, percentiles, quartiles, and box plots as ways to summarize and visualize data distributions. Examples are provided throughout to illustrate statistical concepts.
The document discusses measures of variability in statistics including range, interquartile range, standard deviation, and variance. It provides examples of calculating each measure using sample data sets. The range is the difference between the highest and lowest values, while the interquartile range is the difference between the third and first quartiles. The standard deviation represents the average amount of dispersion from the mean, and variance is the average of the squared deviations from the mean. Both standard deviation and variance increase with greater variability in the data set.
This document provides an overview of statistical tests of significance. It introduces key concepts like data types, measures of central tendency and dispersion, hypotheses, errors, power and level of significance. It discusses parametric tests like t-tests, ANOVA, and correlation coefficients that require normal distribution of data. It also mentions non-parametric tests for non-normal data. The document provides examples and explanations to help understand these important statistical concepts and methods.
Statistics for the Health Scientist: Basic Statistics IIDrLukeKane
This document provides an overview of descriptive statistics concepts including measures of central tendency (mode, median, mean), measures of spread (range, interquartile range, standard deviation), transformation of data to normal distributions, and definitions of prevalence and incidence. It discusses choosing the appropriate statistical measure based on the type of variable and provides examples to illustrate prevalence, incidence, and how to calculate them from population data.
This document outlines key concepts for analyzing qualitative and quantitative data. It discusses preparing data through editing, coding and inserting into a matrix. Graphical techniques like histograms, scatter plots and box plots are presented for depicting individual, comparative and relational data. Measures of central tendency, dispersion, relationships and models are explained including mean, median, standard deviation, correlation, and linear and non-linear models. The goal is for students to understand how to analyze data using appropriate statistical techniques and data visualization.
OER Descriptive Statistics (University of Edinburgh)LeonardHo7
This Open Educational Resource (OER) introduces the concepts of quantitative and qualitative statistics, central tendency (mean, median, and mode), and dispersion (standard deviation and interquartile range).
This document is created for PgCAP Digital Education.
Univariate, bivariate analysis, hypothesis testing, chi squarekongara
This document provides an introduction to data analysis. It discusses various topics related to measurement and types of data, including univariate and bivariate analysis. For univariate analysis, it describes descriptive statistics such as mean, median, mode, variance, and standard deviation. It also discusses data distributions and different measurement scales. For bivariate analysis, it introduces cross-tabulation and chi-square tests to examine relationships between two variables. Cross-tabulation allows looking at associations between variables through frequencies and percentages in tables, while chi-square can be used to test hypotheses about relationships and determine statistical significance.
This document provides an overview of statistical analysis methods for metabolomics data, including data pre-treatment, univariate analysis, and multivariate analysis. It discusses normalization techniques, student's t-tests, volcano plots, principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). The goal of metabolomics data analysis is biomarker discovery and disease diagnosis by identifying significant metabolic features associated with conditions.
This document provides an overview of descriptive statistics concepts including data, variables, observations, and types of data such as quantitative, categorical, cross-sectional, and time series data. It discusses frequency distributions for categorical and quantitative data including histograms. Measures of central tendency like the mean, median, and mode are covered. Measures of variability including range, variance, standard deviation, and coefficient of variation are also introduced. The document concludes with a discussion of percentiles and calculating percentiles from a data set.
This document provides an overview of how to use SPSS to conduct basic statistical analysis and present results. It outlines expectations for the workshop, including learning how to prepare an SPSS file, display and summarize data, and create graphical presentations. The document then covers key SPSS concepts like variables, data types, and examples. It also demonstrates how to perform descriptive statistics, frequency tables, crosstabs, measures of central tendency and dispersion. Finally, it discusses different methods of graphical presentation in SPSS like bar charts, histograms, box plots and more.
One of the most important, yet often overlooked, aspects of predictive modeling is the transformation of data to create model inputs, better known as feature engineering (FE). This talk will go into the theoretical background behind FE, showing how it leverages existing data to produce better modeling results. It will then detail some important FE techniques that should be in every data scientist’s tool kit.
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 discusses interpreting machine learning models and summarizes techniques for interpreting random forests. Random forests are considered "black boxes" due to their complexity but their predictions can be explained by decomposing them into mathematically exact feature contributions. Decision trees can also be interpreted by defining the prediction as a bias plus the contributions from each feature along the decision path. This operational view of decision trees can be extended to interpret random forest predictions despite their complexity.
This document discusses various numerical measures used to describe data, including measures of central tendency (mean, median, mode) and measures of dispersion (range, variance, standard deviation). It provides definitions and formulas for calculating these measures, along with examples of calculating the mean, median, mode, weighted mean, variance, standard deviation, and other concepts. The learning objectives cover explaining central tendency, computing various means, determining the median and mode, calculating the geometric mean, explaining and applying measures of dispersion, computing standard deviation, and concepts like Chebyshev's Theorem and the Empirical Rule.
This document discusses measures of dispersion and the normal distribution. It defines measures of dispersion as ways to quantify the variability in a data set beyond measures of central tendency like mean, median, and mode. The key measures discussed are range, quartile deviation, mean deviation, and standard deviation. It provides formulas and examples for calculating each measure. The document then explains the normal distribution as a theoretical probability distribution important in statistics. It outlines the characteristics of the normal curve and provides examples of using the normal distribution and calculating z-scores.
This document discusses descriptive statistics used to summarize continuous data, including measures of central tendency (mean, median, mode), measures of dispersion (standard deviation, variance, range, interquartile range), and the normal distribution curve. It provides definitions and examples of how to calculate each measure. The key measures are the mean as the average, the median as the middle value, and the mode as the most frequent value. Standard deviation and variance quantify how spread out values are from the mean. Choosing the appropriate central tendency and dispersion measures depends on whether the data distribution is normal/symmetrical or skewed.
Exploratory data analysis (EDA) involves analyzing datasets to discover patterns, trends, and relationships. EDA techniques include graphical methods like histograms, box plots, and scatter plots as well as calculating summary statistics. The goal of EDA is to better understand the data structure and relationships between variables through visual and numerical techniques without beginning with a specific hypothesis. EDA is used to generate hypotheses for further confirmatory analysis and to identify outliers, anomalies, and other unusual data characteristics. Lattice graphics and other plotting functions in R can be useful tools for EDA to visualize univariate and bivariate relationships in data.
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Financial Performance Analysis of Conventional and Non – Conventional Banks.
Hope this will help you. Please don't forget to like/comment. Your appreciation will motivate me to make further more slide. Thanks in advance
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Hope this will help you. Please don't forget to like/comment. Your appreciation will motivate me to make further more slide. Thanks in advance
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STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
3. Content
• Part – 01
1. Frequency distribution table
2. Arithmetic mean, Median, Mode
3. Mean Deviation
4. Population Variance and Population Standard Deviation.
5. Geometric Mean
6. Sample Variance and Sample Standard Deviation.
7. Coefficient of Variation.
• Part – 02
1. Excel output
2. Multicolinearity
6. Questions :
• Constract a frequency distribution table using both inclusive
and exclusive method.
• What is the value of data range?
• Find out Arithmatic mean.
• What is the value of Median?
• Find out mode.
• Determine the Mean Deviation.
• Find out Population Variance and Population Standard
Deviation.
• Determine the Geometric Mean from the above data.
• Find out Sample Variance and Sample Standard Deviation.
• What will be the Coefficient of Variation?
7. •Constract a frequency distribution table:
2k>n
25 (32) >20
So, Number of classes, K=5
i ≥
=
k
LH
02.0
5
0.511.5
9. EXCLUSIVE METHOD (If Interval=3):
Classes Frequency Relative
Frequency
Cumulative
Frequency
5.0 to 5.3 3 0.15 3
5.3 to 5.6 5 0.25 8
5.6 to 5.9 11 0.55 19
5.9 to 5.12 1 0.05 20
5.12 to 6.2 0 0 20
Total 20
19. It is believed that Income per day of a CNG
driver is affected by gas expense per day and
number of trips per day. Results of the multiple
regression equation of Income per day on gas
expense per day and number of trips per day are
given below.
22. • Questions
1) Develop the Hypothesis
2) Find the equation of regression and Interpret.
3) Estimate the relationship among the variables in
relative terms.
4) Assess the explanatory power of the independent
variables.
5) Assess the significance of the results.
6) Ascertain whether there is a problem of
Multicollinearity.