This document provides an introduction to key concepts in statistics including measures of central tendency, variation, distributions, and linear regression. It defines the mean, median, and mode as measures of central tendency. Measures of variation described include range, variance, and standard deviation. Common distributions like the normal distribution are explained and its key properties outlined. Hypothesis testing and p-values are also introduced. Finally, the concepts of covariance, correlation, and simple linear regression models are summarized.
This document discusses measures of central tendency and dispersion used to analyze and summarize data. It defines key terms like mean, median, mode, range, variance, and standard deviation. It explains how to calculate these measures both mathematically and using grouped or sample data, and the importance of understanding the central tendency and dispersion of data distributions.
This document discusses measures of central tendency and dispersion used to analyze and summarize data. It defines key terms like mean, median, mode, range, variance, and standard deviation. It explains how to calculate these measures both mathematically and using grouped or sample data, and the importance of understanding the distribution, central tendency and dispersion of data.
Basic Statistical Descriptions of Data.pptxAnusuya123
This document provides an overview of 7 basic statistical concepts for data science: 1) descriptive statistics such as mean, mode, median, and standard deviation, 2) measures of variability like variance and range, 3) correlation, 4) probability distributions, 5) regression, 6) normal distribution, and 7) types of bias. Descriptive statistics are used to summarize data, variability measures dispersion, correlation measures relationships between variables, and probability distributions specify likelihoods of events. Regression models relationships, normal distribution is often assumed, and biases can influence analyses.
This document provides an overview of biostatistics and various statistical concepts used in dental sciences. It discusses measures of central tendency including mean, median, and mode. It also covers measures of dispersion such as range, mean deviation, and standard deviation. The normal distribution curve and properties are explained. Various statistical tests are mentioned including t-test, ANOVA, chi-square test, and their applications in dental research. Steps for testing hypotheses and types of errors are summarized.
This chapter discusses numerical measures used to describe data, including measures of center (mean, median, mode), location (percentiles, quartiles), and variation (range, variance, standard deviation, coefficient of variation). It defines these terms and how to calculate and interpret them, as well as how to construct and use box and whisker plots to graphically display data distributions.
This document provides an introduction to biostatistics. It defines biostatistics as the branch of statistics dealing with biological data. It discusses different types of data, methods of data presentation including tables, charts and graphs. It also covers measures of central tendency and dispersion, sampling methods, tests of significance including chi-square test and t-test, and correlation and regression. The overall purpose is to introduce basic statistical concepts and methods used for analyzing health and medical data.
This document discusses the normal distribution and standard normal curve. It defines key properties of the normal distribution including that it is bell-shaped and symmetrical around the mean. The standard normal curve is introduced which has a mean of 0 and standard deviation of 1. The z-score is defined as a way to locate a value within a distribution based on its mean and standard deviation. Various probabilities are associated with areas under the normal curve based on z-scores.
Pampers CaseIn an increasingly competitive diaper market, P&G’.docxbunyansaturnina
Pampers Case
In an increasingly competitive diaper market, P&G’s marketing department wanted to formulate new approaches to the construction and marketing of Pampers to position them effectively against Hugggies without cannibalizing Luvs. They surveyed 300 mothers of infants. Each was given a randomly selected brand of diaper (either Pampers, Luvs, or Huggies) and asked to rate that diaper on nine attributes and to give her overall preference for the brand. Preference was obtained on a 7-point Likert scale (1=not at all preferred, 7=greatly preferred). Diaper ratings on the nine attributes were also obtained on 7-point Likert scale (1=very unfavorable, 7=very favorable). The study was designed so that each of the three brands appeared 100 times. The goal of the study was to learn which attributes of diapers were most important in influencing purchase preference (Y). The nine attributes used in study were:
Variable
Attribute
Marketing options
X1
count per box
Desire large counts per box?
X2
price
Pay a premium price?
X3
value
Promote high value
X4
skin care
Offer high degree of skin care
X5
style
Prints/color vs. plain diapers
X6
absorbency
Regular vs. superabsorbency
X7
leakage
Narrow/tapered vs. regular crotch
X8
comfort/size
Extra padding and form-fitting gathers
X9
taping
Re-sealable tape vs. regular tape
Question (will be discussed in week 8):
If you don’t have SPSS software at home, you may be able to download a trial version (good for 21 days) from spss.com(software(statistics family(PASW statistics 17.0(click “free trial” and download.
1. Run a regression analysis for brand preference that includes all independent variables in the model, and describe how meaningful the model is. Interpret the results for management.
6. Correlation and Regression
*
The mean, or average value, is the most commonly used measure of central tendency. The mean, ,is given by
Where,
Xi = Observed values of the variable X
n = Number of observations (sample size)
The mode is the value that occurs most frequently. It represents the highest peak of the distribution. The mode is a good measure of location when the variable is inherently categorical or has otherwise been grouped into categories.
Statistics Associated with Frequency Distribution Measures of Location
X
=
X
i
/
n
S
i
=
1
n
X
*
The median of a sample is the middle value when the data are arranged in ascending or descending order.
http://www.city-data.com/
Statistics Associated with Frequency Distribution Measures of Location
*
Skewness. The tendency of the deviations from the mean to be larger in one direction than in the other. It can be thought of as the tendency for one tail of the distribution to be heavier than the other.
Kurtosis is a measure of the relative peakedness or flatness of the curve defined by the frequency distribution. The kurtosis of a normal distribution is zero. If.
This document discusses measures of central tendency and dispersion used to analyze and summarize data. It defines key terms like mean, median, mode, range, variance, and standard deviation. It explains how to calculate these measures both mathematically and using grouped or sample data, and the importance of understanding the central tendency and dispersion of data distributions.
This document discusses measures of central tendency and dispersion used to analyze and summarize data. It defines key terms like mean, median, mode, range, variance, and standard deviation. It explains how to calculate these measures both mathematically and using grouped or sample data, and the importance of understanding the distribution, central tendency and dispersion of data.
Basic Statistical Descriptions of Data.pptxAnusuya123
This document provides an overview of 7 basic statistical concepts for data science: 1) descriptive statistics such as mean, mode, median, and standard deviation, 2) measures of variability like variance and range, 3) correlation, 4) probability distributions, 5) regression, 6) normal distribution, and 7) types of bias. Descriptive statistics are used to summarize data, variability measures dispersion, correlation measures relationships between variables, and probability distributions specify likelihoods of events. Regression models relationships, normal distribution is often assumed, and biases can influence analyses.
This document provides an overview of biostatistics and various statistical concepts used in dental sciences. It discusses measures of central tendency including mean, median, and mode. It also covers measures of dispersion such as range, mean deviation, and standard deviation. The normal distribution curve and properties are explained. Various statistical tests are mentioned including t-test, ANOVA, chi-square test, and their applications in dental research. Steps for testing hypotheses and types of errors are summarized.
This chapter discusses numerical measures used to describe data, including measures of center (mean, median, mode), location (percentiles, quartiles), and variation (range, variance, standard deviation, coefficient of variation). It defines these terms and how to calculate and interpret them, as well as how to construct and use box and whisker plots to graphically display data distributions.
This document provides an introduction to biostatistics. It defines biostatistics as the branch of statistics dealing with biological data. It discusses different types of data, methods of data presentation including tables, charts and graphs. It also covers measures of central tendency and dispersion, sampling methods, tests of significance including chi-square test and t-test, and correlation and regression. The overall purpose is to introduce basic statistical concepts and methods used for analyzing health and medical data.
This document discusses the normal distribution and standard normal curve. It defines key properties of the normal distribution including that it is bell-shaped and symmetrical around the mean. The standard normal curve is introduced which has a mean of 0 and standard deviation of 1. The z-score is defined as a way to locate a value within a distribution based on its mean and standard deviation. Various probabilities are associated with areas under the normal curve based on z-scores.
Pampers CaseIn an increasingly competitive diaper market, P&G’.docxbunyansaturnina
Pampers Case
In an increasingly competitive diaper market, P&G’s marketing department wanted to formulate new approaches to the construction and marketing of Pampers to position them effectively against Hugggies without cannibalizing Luvs. They surveyed 300 mothers of infants. Each was given a randomly selected brand of diaper (either Pampers, Luvs, or Huggies) and asked to rate that diaper on nine attributes and to give her overall preference for the brand. Preference was obtained on a 7-point Likert scale (1=not at all preferred, 7=greatly preferred). Diaper ratings on the nine attributes were also obtained on 7-point Likert scale (1=very unfavorable, 7=very favorable). The study was designed so that each of the three brands appeared 100 times. The goal of the study was to learn which attributes of diapers were most important in influencing purchase preference (Y). The nine attributes used in study were:
Variable
Attribute
Marketing options
X1
count per box
Desire large counts per box?
X2
price
Pay a premium price?
X3
value
Promote high value
X4
skin care
Offer high degree of skin care
X5
style
Prints/color vs. plain diapers
X6
absorbency
Regular vs. superabsorbency
X7
leakage
Narrow/tapered vs. regular crotch
X8
comfort/size
Extra padding and form-fitting gathers
X9
taping
Re-sealable tape vs. regular tape
Question (will be discussed in week 8):
If you don’t have SPSS software at home, you may be able to download a trial version (good for 21 days) from spss.com(software(statistics family(PASW statistics 17.0(click “free trial” and download.
1. Run a regression analysis for brand preference that includes all independent variables in the model, and describe how meaningful the model is. Interpret the results for management.
6. Correlation and Regression
*
The mean, or average value, is the most commonly used measure of central tendency. The mean, ,is given by
Where,
Xi = Observed values of the variable X
n = Number of observations (sample size)
The mode is the value that occurs most frequently. It represents the highest peak of the distribution. The mode is a good measure of location when the variable is inherently categorical or has otherwise been grouped into categories.
Statistics Associated with Frequency Distribution Measures of Location
X
=
X
i
/
n
S
i
=
1
n
X
*
The median of a sample is the middle value when the data are arranged in ascending or descending order.
http://www.city-data.com/
Statistics Associated with Frequency Distribution Measures of Location
*
Skewness. The tendency of the deviations from the mean to be larger in one direction than in the other. It can be thought of as the tendency for one tail of the distribution to be heavier than the other.
Kurtosis is a measure of the relative peakedness or flatness of the curve defined by the frequency distribution. The kurtosis of a normal distribution is zero. If.
This document discusses various methods for analyzing and presenting data. It covers descriptive statistics such as measures of central tendency (mean, median, mode) and variability (variance, standard deviation, range). It also discusses relational statistics like univariate, bivariate, and multivariate analysis, as well as correlation. Graphical methods like histograms and frequency distributions are presented as ways to visually depict raw data and relationships. Inferential statistics involving difference of means tests and assessing statistical significance are also outlined.
MSC III_Research Methodology and Statistics_Inferrential ststistics.pdfSuchita Rawat
This document discusses various statistical measures of dispersion and relationships. It defines dispersion as describing how spread out a set of data is, and lists common measures including range, variance, standard deviation, and interquartile range. It also discusses relative measures that allow comparison between datasets, and measures of relationships like covariance and correlation that indicate the strength and direction of relationships between variables. Finally, it provides formulas and explanations of common statistical tests like t-tests, chi-square tests, ANOVA, and simple and multiple linear regression analyses.
1. The document discusses key concepts in biostatistics including measures of central tendency, dispersion, correlation, regression, and sampling.
2. Measures of central tendency described are the mean, median, and mode. Measures of dispersion include range, standard deviation, and quartile deviation.
3. The importance of statistical analysis for living organisms in areas like medicine, biology and public health is highlighted. Examples are provided to demonstrate calculation of statistical measures.
This document discusses measures of central tendency including the mean, median, and mode. It provides definitions and formulas for calculating each measure. The mean is the average and is calculated by summing all values and dividing by the total number of items. The median is the middle value when items are arranged from lowest to highest. The mode is the value that occurs most frequently in a data set. Examples are given to demonstrate calculating each measure using raw data.
Overview of Advance Marketing ResearchEnamul Islam
This document provides information on frequency distributions, cross-tabulation, hypothesis testing, and analysis of variance. It defines key terms like frequency distribution, measures of location and variability, cross-tabulation, chi-square test, and one-way ANOVA. It also outlines the general procedures for hypothesis testing and conducting one-way ANOVA, including decomposing total variation, measuring effects, and interpreting results.
MSC III_Research Methodology and Statistics_Descriptive statistics.pdfSuchita Rawat
This document discusses key concepts in research methodology and statistics. It defines statistics as dealing with the collection, analysis, and interpretation of quantitative and qualitative data. It then discusses various types of graphs used to visually represent data, such as bar graphs, pie charts, histograms, boxplots, and scatterplots. It also defines common measures of central tendency (mean, median, mode), dispersion (range, variance, standard deviation, IQR), and skewness.
Biostatistics is the science of collecting, summarizing, analyzing, and interpreting data in the fields of medicine, biology, and public health. It involves both descriptive and inferential statistics. Descriptive statistics summarize data through measures of central tendency like mean, median, and mode, and measures of dispersion like range and standard deviation. Inferential statistics allow generalization from samples to populations through techniques like hypothesis testing, confidence intervals, and estimation. Sample size determination and random sampling help ensure validity and minimize errors in statistical analyses.
Variables describe attributes that can vary between entities. They can be qualitative (categorical) or quantitative (numeric). Common types of variables include continuous, discrete, ordinal, and nominal variables. Data can be presented graphically through bar charts, pie charts, histograms, box plots, and scatter plots to better understand patterns and trends. Key measures used to summarize data include measures of central tendency (mean, median, mode) and measures of variability (range, standard deviation, interquartile range).
Descriptive statistics are used to summarize and describe characteristics of a data set. It includes measures of central tendency like mean, median, and mode, measures of variability like range and standard deviation, and the distribution of data through histograms. Inferential statistics are used to generalize results from a sample to the population it represents through estimation of population parameters and hypothesis testing. Correlation and regression analysis are used to study relationships between two or more variables.
This document discusses descriptive statistics and provides information on various descriptive statistics measures. It defines descriptive statistics as means of organizing and summarizing observations. It describes different types of descriptive statistics including measures of central tendency such as mean, median and mode, and measures of dispersion such as range, variance, standard deviation and interquartile range. Examples are provided to demonstrate how to calculate mean, median and mode from a data set. Additional measures like percentiles, quartiles, boxplots, skewness and kurtosis are also explained.
This document discusses various measures of central tendency and dispersion that are commonly used in epidemiology to summarize data distributions. It describes the mean, median and mode as measures of central tendency that convey the average or typical value, and how the appropriate measure depends on the data's measurement level, shape and research purpose. Measures of dispersion like range, interquartile range, variance and standard deviation describe how spread out the data is from the central value. The document provides formulas and explanations for calculating and interpreting each measure.
marketing research & applications on SPSSANSHU TIWARI
The document discusses various statistical techniques used in marketing research to analyze survey data, including frequency distributions, measures of central tendency and variability, hypothesis testing, and cross-tabulation. Frequency distributions are used to determine the mean, mode, median and answer questions about single variables. Hypothesis testing involves forming hypotheses, selecting a test, determining significance levels, collecting data, and making statistical decisions. Cross-tabulation examines relationships between two or more variables using techniques like chi-square tests. Both parametric and non-parametric tests are used depending on variable scales.
The document discusses various measures of variability that can be used to describe the spread or dispersion of data, including the range, interquartile range, mean absolute deviation, variance, standard deviation, and coefficient of variation. It also covers how to calculate and interpret these measures of variability for both ungrouped and grouped data. Various other concepts are introduced such as the empirical rule, z-scores, skewness, the 5-number summary, and how to construct and interpret a box-and-whisker plot.
This document discusses descriptive statistics used in research. It defines descriptive statistics as procedures used to organize, interpret, and communicate numeric data. Key aspects covered include frequency distributions, measures of central tendency (mode, median, mean), measures of variability, bivariate descriptive statistics using contingency tables and correlation, and describing risk to facilitate evidence-based decision making. The overall purpose of descriptive statistics is to synthesize and summarize quantitative data for analysis in research.
The document discusses various measures of central tendency and variability used in descriptive statistics. It defines the mean as the sum of all values divided by the number of values. The median is the middle value when values are sorted in ascending order. The mode is the most frequently occurring value. Variability measures the dispersion of scores around the mean and includes the range, interquartile range, standard deviation, and variance. The interquartile range is the difference between the third and first quartiles. Covariance measures how two variables vary together and is used to calculate the correlation coefficient. Factors like extreme scores, sample size, stability under sampling, and open-ended distributions can affect measures of variability.
The document discusses key concepts in statistics including frequency distributions, measures of central tendency, measures of dispersion, kurtosis, and skewness. It defines a frequency distribution as a representation of data in graphical or tabular form that displays the frequency of observations within intervals. Measures of central tendency discussed are the mode, median, and mean, while measures of dispersion include range, interquartile range, and standard deviation. Kurtosis and skewness describe the shape of a distribution - kurtosis refers to peakedness compared to a normal curve, while skewness indicates symmetrical vs asymmetrical tails.
This document provides an overview of basic statistics concepts. It defines statistics as the science of collecting, analyzing, and interpreting data. There are two main types of statistics: descriptive statistics which summarize data, and inferential statistics which make predictions from data. Key concepts discussed include variables, frequency distributions, measures of center such as mean and median, measures of variability such as range and standard deviation, and methods of presenting data graphically and numerically.
This document provides an overview of basic statistics concepts. It defines statistics as the science of collecting, analyzing, and interpreting data. There are two main types of statistics: descriptive statistics which summarize data, and inferential statistics which make predictions from data. Key concepts discussed include variables, frequency distributions, measures of center such as mean and median, measures of variability such as range and standard deviation, and methods of presenting data graphically and numerically.
Prescriptive analytics BA4206 Anna University PPTFreelance
Business analysis - Prescriptive analytics Introduction to Prescriptive analytics
Prescriptive Modeling
Non Linear Optimization
Demonstrating Business Performance Improvement
This document discusses various methods for analyzing and presenting data. It covers descriptive statistics such as measures of central tendency (mean, median, mode) and variability (variance, standard deviation, range). It also discusses relational statistics like univariate, bivariate, and multivariate analysis, as well as correlation. Graphical methods like histograms and frequency distributions are presented as ways to visually depict raw data and relationships. Inferential statistics involving difference of means tests and assessing statistical significance are also outlined.
MSC III_Research Methodology and Statistics_Inferrential ststistics.pdfSuchita Rawat
This document discusses various statistical measures of dispersion and relationships. It defines dispersion as describing how spread out a set of data is, and lists common measures including range, variance, standard deviation, and interquartile range. It also discusses relative measures that allow comparison between datasets, and measures of relationships like covariance and correlation that indicate the strength and direction of relationships between variables. Finally, it provides formulas and explanations of common statistical tests like t-tests, chi-square tests, ANOVA, and simple and multiple linear regression analyses.
1. The document discusses key concepts in biostatistics including measures of central tendency, dispersion, correlation, regression, and sampling.
2. Measures of central tendency described are the mean, median, and mode. Measures of dispersion include range, standard deviation, and quartile deviation.
3. The importance of statistical analysis for living organisms in areas like medicine, biology and public health is highlighted. Examples are provided to demonstrate calculation of statistical measures.
This document discusses measures of central tendency including the mean, median, and mode. It provides definitions and formulas for calculating each measure. The mean is the average and is calculated by summing all values and dividing by the total number of items. The median is the middle value when items are arranged from lowest to highest. The mode is the value that occurs most frequently in a data set. Examples are given to demonstrate calculating each measure using raw data.
Overview of Advance Marketing ResearchEnamul Islam
This document provides information on frequency distributions, cross-tabulation, hypothesis testing, and analysis of variance. It defines key terms like frequency distribution, measures of location and variability, cross-tabulation, chi-square test, and one-way ANOVA. It also outlines the general procedures for hypothesis testing and conducting one-way ANOVA, including decomposing total variation, measuring effects, and interpreting results.
MSC III_Research Methodology and Statistics_Descriptive statistics.pdfSuchita Rawat
This document discusses key concepts in research methodology and statistics. It defines statistics as dealing with the collection, analysis, and interpretation of quantitative and qualitative data. It then discusses various types of graphs used to visually represent data, such as bar graphs, pie charts, histograms, boxplots, and scatterplots. It also defines common measures of central tendency (mean, median, mode), dispersion (range, variance, standard deviation, IQR), and skewness.
Biostatistics is the science of collecting, summarizing, analyzing, and interpreting data in the fields of medicine, biology, and public health. It involves both descriptive and inferential statistics. Descriptive statistics summarize data through measures of central tendency like mean, median, and mode, and measures of dispersion like range and standard deviation. Inferential statistics allow generalization from samples to populations through techniques like hypothesis testing, confidence intervals, and estimation. Sample size determination and random sampling help ensure validity and minimize errors in statistical analyses.
Variables describe attributes that can vary between entities. They can be qualitative (categorical) or quantitative (numeric). Common types of variables include continuous, discrete, ordinal, and nominal variables. Data can be presented graphically through bar charts, pie charts, histograms, box plots, and scatter plots to better understand patterns and trends. Key measures used to summarize data include measures of central tendency (mean, median, mode) and measures of variability (range, standard deviation, interquartile range).
Descriptive statistics are used to summarize and describe characteristics of a data set. It includes measures of central tendency like mean, median, and mode, measures of variability like range and standard deviation, and the distribution of data through histograms. Inferential statistics are used to generalize results from a sample to the population it represents through estimation of population parameters and hypothesis testing. Correlation and regression analysis are used to study relationships between two or more variables.
This document discusses descriptive statistics and provides information on various descriptive statistics measures. It defines descriptive statistics as means of organizing and summarizing observations. It describes different types of descriptive statistics including measures of central tendency such as mean, median and mode, and measures of dispersion such as range, variance, standard deviation and interquartile range. Examples are provided to demonstrate how to calculate mean, median and mode from a data set. Additional measures like percentiles, quartiles, boxplots, skewness and kurtosis are also explained.
This document discusses various measures of central tendency and dispersion that are commonly used in epidemiology to summarize data distributions. It describes the mean, median and mode as measures of central tendency that convey the average or typical value, and how the appropriate measure depends on the data's measurement level, shape and research purpose. Measures of dispersion like range, interquartile range, variance and standard deviation describe how spread out the data is from the central value. The document provides formulas and explanations for calculating and interpreting each measure.
marketing research & applications on SPSSANSHU TIWARI
The document discusses various statistical techniques used in marketing research to analyze survey data, including frequency distributions, measures of central tendency and variability, hypothesis testing, and cross-tabulation. Frequency distributions are used to determine the mean, mode, median and answer questions about single variables. Hypothesis testing involves forming hypotheses, selecting a test, determining significance levels, collecting data, and making statistical decisions. Cross-tabulation examines relationships between two or more variables using techniques like chi-square tests. Both parametric and non-parametric tests are used depending on variable scales.
The document discusses various measures of variability that can be used to describe the spread or dispersion of data, including the range, interquartile range, mean absolute deviation, variance, standard deviation, and coefficient of variation. It also covers how to calculate and interpret these measures of variability for both ungrouped and grouped data. Various other concepts are introduced such as the empirical rule, z-scores, skewness, the 5-number summary, and how to construct and interpret a box-and-whisker plot.
This document discusses descriptive statistics used in research. It defines descriptive statistics as procedures used to organize, interpret, and communicate numeric data. Key aspects covered include frequency distributions, measures of central tendency (mode, median, mean), measures of variability, bivariate descriptive statistics using contingency tables and correlation, and describing risk to facilitate evidence-based decision making. The overall purpose of descriptive statistics is to synthesize and summarize quantitative data for analysis in research.
The document discusses various measures of central tendency and variability used in descriptive statistics. It defines the mean as the sum of all values divided by the number of values. The median is the middle value when values are sorted in ascending order. The mode is the most frequently occurring value. Variability measures the dispersion of scores around the mean and includes the range, interquartile range, standard deviation, and variance. The interquartile range is the difference between the third and first quartiles. Covariance measures how two variables vary together and is used to calculate the correlation coefficient. Factors like extreme scores, sample size, stability under sampling, and open-ended distributions can affect measures of variability.
The document discusses key concepts in statistics including frequency distributions, measures of central tendency, measures of dispersion, kurtosis, and skewness. It defines a frequency distribution as a representation of data in graphical or tabular form that displays the frequency of observations within intervals. Measures of central tendency discussed are the mode, median, and mean, while measures of dispersion include range, interquartile range, and standard deviation. Kurtosis and skewness describe the shape of a distribution - kurtosis refers to peakedness compared to a normal curve, while skewness indicates symmetrical vs asymmetrical tails.
This document provides an overview of basic statistics concepts. It defines statistics as the science of collecting, analyzing, and interpreting data. There are two main types of statistics: descriptive statistics which summarize data, and inferential statistics which make predictions from data. Key concepts discussed include variables, frequency distributions, measures of center such as mean and median, measures of variability such as range and standard deviation, and methods of presenting data graphically and numerically.
This document provides an overview of basic statistics concepts. It defines statistics as the science of collecting, analyzing, and interpreting data. There are two main types of statistics: descriptive statistics which summarize data, and inferential statistics which make predictions from data. Key concepts discussed include variables, frequency distributions, measures of center such as mean and median, measures of variability such as range and standard deviation, and methods of presenting data graphically and numerically.
Prescriptive analytics BA4206 Anna University PPTFreelance
Business analysis - Prescriptive analytics Introduction to Prescriptive analytics
Prescriptive Modeling
Non Linear Optimization
Demonstrating Business Performance Improvement
The Steadfast and Reliable Bull: Taurus Zodiac Signmy Pandit
Explore the steadfast and reliable nature of the Taurus Zodiac Sign. Discover the personality traits, key dates, and horoscope insights that define the determined and practical Taurus, and learn how their grounded nature makes them the anchor of the zodiac.
Unlocking WhatsApp Marketing with HubSpot: Integrating Messaging into Your Ma...Niswey
50 million companies worldwide leverage WhatsApp as a key marketing channel. You may have considered adding it to your marketing mix, or probably already driving impressive conversions with WhatsApp.
But wait. What happens when you fully integrate your WhatsApp campaigns with HubSpot?
That's exactly what we explored in this session.
We take a look at everything that you need to know in order to deploy effective WhatsApp marketing strategies, and integrate it with your buyer journey in HubSpot. From technical requirements to innovative campaign strategies, to advanced campaign reporting - we discuss all that and more, to leverage WhatsApp for maximum impact. Check out more details about the event here https://events.hubspot.com/events/details/hubspot-new-delhi-presents-unlocking-whatsapp-marketing-with-hubspot-integrating-messaging-into-your-marketing-strategy/
Ellen Burstyn: From Detroit Dreamer to Hollywood Legend | CIO Women MagazineCIOWomenMagazine
In this article, we will dive into the extraordinary life of Ellen Burstyn, where the curtains rise on a story that's far more attractive than any script.
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The Most Inspiring Entrepreneurs to Follow in 2024.pdfthesiliconleaders
In a world where the potential of youth innovation remains vastly untouched, there emerges a guiding light in the form of Norm Goldstein, the Founder and CEO of EduNetwork Partners. His dedication to this cause has earned him recognition as a Congressional Leadership Award recipient.
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[To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a curated compilation of PowerPoint diagrams and templates designed to illustrate 20 different digital transformation frameworks and models. These frameworks are based on recent industry trends and best practices, ensuring that the content remains relevant and up-to-date.
Key highlights include Microsoft's Digital Transformation Framework, which focuses on driving innovation and efficiency, and McKinsey's Ten Guiding Principles, which provide strategic insights for successful digital transformation. Additionally, Forrester's framework emphasizes enhancing customer experiences and modernizing IT infrastructure, while IDC's MaturityScape helps assess and develop organizational digital maturity. MIT's framework explores cutting-edge strategies for achieving digital success.
These materials are perfect for enhancing your business or classroom presentations, offering visual aids to supplement your insights. Please note that while comprehensive, these slides are intended as supplementary resources and may not be complete for standalone instructional purposes.
Frameworks/Models included:
Microsoft’s Digital Transformation Framework
McKinsey’s Ten Guiding Principles of Digital Transformation
Forrester’s Digital Transformation Framework
IDC’s Digital Transformation MaturityScape
MIT’s Digital Transformation Framework
Gartner’s Digital Transformation Framework
Accenture’s Digital Strategy & Enterprise Frameworks
Deloitte’s Digital Industrial Transformation Framework
Capgemini’s Digital Transformation Framework
PwC’s Digital Transformation Framework
Cisco’s Digital Transformation Framework
Cognizant’s Digital Transformation Framework
DXC Technology’s Digital Transformation Framework
The BCG Strategy Palette
McKinsey’s Digital Transformation Framework
Digital Transformation Compass
Four Levels of Digital Maturity
Design Thinking Framework
Business Model Canvas
Customer Journey Map
During the budget session of 2024-25, the finance minister, Nirmala Sitharaman, introduced the “solar Rooftop scheme,” also known as “PM Surya Ghar Muft Bijli Yojana.” It is a subsidy offered to those who wish to put up solar panels in their homes using domestic power systems. Additionally, adopting photovoltaic technology at home allows you to lower your monthly electricity expenses. Today in this blog we will talk all about what is the PM Surya Ghar Muft Bijli Yojana. How does it work? Who is eligible for this yojana and all the other things related to this scheme?
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2. The Mean
Population X1, X2, …, XN
m
Population Mean
N
X
N
=1
i
i
Sample x1, x2, …, xn
Sample Mean
x
n
x
x
n
=1
i
i
3-2
3. The Sample Mean
and is a point estimate of the population mean
n
x
x
x
n
x
x n
n
i
i
...
2
1
1
For a sample of size n, the sample mean (x) is defined as
3-3
Population mean (μ) is average of the population measurements
4. Descriptive Statistics
Measures of Location or measures of central tendency
These measures are summary statistics that represent the
center point or typical value of data
Mean (Arithmetic Mean): The most used measure of location
is the mean (arithmetic mean) or average value.
Median: The median is the value in the middle when the data
are arranged in ascending order. It is the middle value, for an
odd number of data and it is the average of two middle values
for an even number of observations.
Mode: The mode is the value that occurs most frequently in a
data set. If all the data points have a frequency of one, there is
no mode. If the greatest frequency occurs at two or more
different values, there is more than one mode.
5. Properties of the Normal
Distribution
The shape of any individual normal curve depends on its
specific mean and standard deviation s
The highest point is over the mean
Mean = median = mode
All measures of central tendency equal each other
The curve is symmetrical about its mean
The left and right halves of the curve are mirror
images
6-5
7. Measures of Variation
Knowing the measures of central tendency is not
enough
Both of the distributions below have identical
measures of central tendency
3-7
8. Measures of Variation
Range Largest minus the smallest
measurement
Variance The average of the squared deviations
of all the population measurements from
the population mean
Standard The square root of the variance
Deviation
3-8
9. Descriptive Statistics
Measures of Variability
Measures how different the values or variation in data are in a data set
Range: Range is the difference between the largest and smallest values in a
data set. Easy to understand but it ignores all other data points in between
and the way data are distributed.
Variance: Variance is the average of the squared differences between each
data value and the mean. It is based on the difference between the value of
each observation (xi) and the mean (x¯ for a sample and μ for a
population). Population variance is denoted by σ2 and sample variance
denoted by s2.
Standard Deviation: Since the units associated with the variance (squared
of the unit of the data) often cause confusion and difficult understanding,
the square root of the variance is defined as the standard
deviation. Population standard deviation denoted by σ and sample standard
deviation denoted by s.
10. Hypothesis
The null hypothesis and alternative hypotheses are
statements regarding the differences or effects that
occur in the population.
The null hypothesis assumes that whatever you are
trying to prove did not happen.
Null Hypotheses (H0): Undertaking seminar classes has
no effect on students' performance.
Alternative Hypothesis (HA): Undertaking seminar
class has a positive effect on students' performance.
significance levels to find evidence for either the null or
alternative hypothesis
11. P-value
Also known as level of significance
Accepted p – value is 0.05
If p-value is 0.03 (i.e., p = .03), this means that
there is a 3% chance of finding a difference as
large as (or larger than) the one in your study
given that the null hypothesis is true.
12. Distribution Shapes
Symmetrical and rectangular
The uniform distribution
Symmetrical and bell-shaped
The normal distribution
Skewed
Skewed either left or right
6-12
13. Normal curve
is a bell-shaped curve which shows the
probability distribution of a continuous
random variable
represents the distribution of values,
frequencies, or probabilities of a set of data.
6-13
14. The Normal Probability
Distribution Continued
The normal curve is symmetrical
about its mean
The mean is in the middle under the
curve
So is also the median
It is tallest over its mean
The area under the entire normal
curve is 1
The area under either half of the curve
is 0.5
6-14
15. Properties of the Normal
Distribution
The shape of any individual normal curve depends
on its specific mean and standard deviation s
The highest point is over the mean
Mean = median = mode
All measures of central tendency equal each other
The curve is symmetrical about its mean
The left and right halves of the curve are mirror images
6-15
16. Properties of the Normal
Distribution Continued
The tails of the normal extend to infinity in
both directions
The tails get closer to the horizontal axis but
never touch it
The area under the normal curve to the right
of the mean equals the area under the
normal to the left of the mean
The area under each half is 0.5
6-16
18. The Empirical Rule for
Normal Populations
If a population has mean µ and standard
deviation σ and is described by a normal
curve, then
68.26% of the population measurements lie within
one standard deviation of the mean: [µ-σ, µ+σ]
95.44% of the population measurements lie within
two standard deviations of the mean: [µ-2σ, µ+2σ]
99.73% of the population measurements lie within
three standard deviations of the mean: [µ-3σ,
µ+3σ]
3-18
19. Percentiles, Quartiles, and Box-
and-Whiskers Displays
For a set of measurements arranged in increasing
order, the pth percentile is a value such that p
percent of the measurements fall at or below the
value and (100-p) percent of the measurements fall
at or above the value
The first quartile Q1 is the 25th percentile
The second quartile (median) is the 50th percentile
The third quartile Q3 is the 75th percentile
The interquartile range IQR is Q3 - Q1
3-19
20. Five Number Summary
1. The smallest
measurement
2. The first quartile, Q1
3. The median, Md
4. The third quartile, Q3
5. The largest
measurement
Displayed visually
using a box-and-
whiskers plot
3-20
21. Box-and-Whiskers Plots
The box plots the:
First quartile, Q1
Median, Md
Third quartile, Q3
Inner fences
Outer fences
Inner fences
Located 1.5IQR away
from the quartiles:
Q1 – (1.5 IQR)
Q3 + (1.5 IQR)
Outer fences
Located 3IQR away
from the quartiles:
Q1 – (3 IQR)
Q3 + (3 IQR)
3-21
22. Box-and-Whiskers Plots Continued
The “whiskers” are dashed lines that plot the
range of the data
A dashed line drawn from the box below Q1 down
to the smallest measurement
Another dashed line drawn from the box above Q3
up to the largest measurement
3-22
23. Outliers
Outliers are measurements that are very
different from other measurements
They are either much larger or much smaller than
most of the other measurements
Outliers lie beyond the fences of the box-and-
whiskers plot
Measurements between the inner and outer
fences are mild outliers
Measurements beyond the outer fences are
severe outliers
3-23
24. Covariance
A measure of the strength of a linear
relationship is the covariance
A positive covariance indicates a positive
linear relationship between x and y
As x increases, y increases
A negative covariance indicates a negative
linear relationship between x and y
As x increases, y decreases
3-24
25. Correlation Coefficient
Magnitude of covariance does not indicate
the strength of the relationship
Magnitude depends on the unit of measurement
used for the data
Correlation coefficient (r) is a measure of the
strength of the relationship that does not
depend on the magnitude of the data
y
x
xy
s
s
s
r
3-25
26. Correlation Coefficient Continued
Sample correlation coefficient r is always
between -1 and +1
Values near -1 show strong negative correlation
Values near 0 show no correlation
Values near +1 show strong positive correlation
3-26
28. The Simple Linear Regression
Model and the Least Squares
Point Estimates
The dependent (or response) variable is the
variable we wish to understand or predict
The independent (or predictor) variable is the
variable we will use to understand or predict the
dependent variable
Regression analysis is a statistical technique that
uses observed data to relate the dependent variable
to one or more independent variables
The objective is to build a regression model that can
describe, predict and control the dependent variable
based on the independent variable
13-28
29. Form of The Simple Linear
Regression Model
y = β0 + β1x + ε
y = β0 + β1x + ε is the mean value of the dependent
variable y when the value of the independent
variable is x
β0 is the y-intercept; the mean of y when x is 0
β1 is the slope; the change in the mean of y per unit
change in x
ε is an error term that describes the effect on y of all
factors other than x
13-29
30. Regression Terms
β0 and β1 are called regression parameters
β0 is the y-intercept and β1 is the slope
We do not know the true values of these
parameters
So, we must use sample data to estimate
them
b0 is the estimate of β0 and b1 is the estimate
of β1
13-30
32. Simple Coefficient of
Determination and Correlation
How useful is a particular regression model?
One measure of usefulness is the simple
coefficient of determination
It is represented by the symbol r2
13-32
33. Calculating The Simple
Coefficient of Determination
1. Total variation is given by the formula
(yi-ȳ)2
2. Explained variation is given by the formula (ŷi-
ȳ)2
3. Unexplained variation is given by the formula (yi-
ŷ)2
4. Total variation is the sum of explained and
unexplained variation
5. r2 is the ratio of explained variation to total
variation
13-33
34. The Multiple Regression Model
Simple linear regression used one independent
variable to explain the dependent variable
Some relationships are too complex to be described using
a single independent variable
Multiple regression uses two or more independent
variables to describe the dependent variable
This allows multiple regression models to handle more
complex situations
There is no limit to the number of independent variables a
model can use
Multiple regression has only one dependent variable
14-34
35. The Multiple Regression
Model
• The linear regression model relating y to x1, x2,…, xk is y =
β0 + β1x1 + β2x2 +…+ βkxk +
• µy = β0 + β1x1 + β2x2 +…+ βkxk is the mean value of the
dependent variable y when the values of the independent
variables are x1, x2,…, xk
• β0, β1, β2,… βk are unknown the regression parameters
relating the mean value of y to x1, x2,…, xk
• is an error term that describes the effects on y of all
factors other than the independent variables x1, x2,…, xk
14-35
37. Model Assumptions and
the Standard Error
The model is
y = β0 + β1x1 + β2x2 + … + βkxk +
Assumptions for multiple regression are
stated about the model error terms, ’s
14-37
38. R2 and Adjusted R2 Continued
5. The multiple coefficient of determination is
the ratio of explained variation to total
variation
6. R2 is the proportion of the total variation that
is explained by the overall regression model
7. Multiple correlation coefficient R is the
square root of R2
14-38
39. The Adjusted R2
Adding an independent variable to multiple
regression will raise R2
R2 will rise slightly even if the new variable has no
relationship to y
The adjusted R2 corrects this tendency in R2
As a result, it gives a better estimate of the
importance of the independent variables
14-39