Download presentation

Published byRebecca Stevens Modified over 3 years ago

1
**Copyright © 2014 by McGraw-Hill Higher Education. All rights reserved.**

Essentials of Business Statistics: Communicating with Numbers By Sanjiv Jaggia and Alison Kelly Copyright © 2014 by McGraw-Hill Higher Education. All rights reserved.

2
**Numerical Descriptive Measures**

Chapter 3 Learning Objectives LO 3.1 Calculate and interpret the mean, the median, and the mode. LO 3.2 Calculate and interpret percentiles and a box plot. LO 3.3 Calculate and interpret the range, the mean absolute deviation, the variance, the standard deviation, and the coefficient of variation. LO 3.4 Explain mean-variance analysis and the Sharpe ratio. LO 3.5 Apply Chebyshev’s Theorem, the empirical rule, and z-scores. LO 3.6 Calculate the mean and the variance for grouped data. LO 3.7 Calculate and interpret the covariance and the correlation coefficient. Numerical Descriptive Measures

3
**Numerical Descriptive Measures**

3.1 Measures of Central Location LO 3.1 Calculate and interpret the arithmetic mean, the median, and the mode. The arithmetic mean is a primary measure of central location. Sample Mean Population Mean m Numerical Descriptive Measures

4
**Numerical Descriptive Measures**

LO 3.1 3.1 Measures of Central Location The mean is sensitive to outliers. Consider the salaries of employees at Acetech. This mean does not reflect the typical salary! Numerical Descriptive Measures

5
**Numerical Descriptive Measures**

LO 3.1 3.1 Measures of Central Location The median is another measure of central location that is not affected by outliers. When the data are arranged in ascending order, the median is the middle value if the number of observations is odd, or the average of the two middle values if the number of observations is even. Numerical Descriptive Measures

6
**Numerical Descriptive Measures**

LO 3.1 3.1 Measures of Central Location The mode is another measure of central location. The most frequently occurring value in a data set Used to summarize qualitative data A data set can have no mode, one mode (unimodal), or many modes (multimodal). Consider the salary of employees at Acetech The mode is $40,000 since this value appears most often. Numerical Descriptive Measures

7
**Numerical Descriptive Measures**

LO 3.1 3.1 Measures of Central Location Weighted Mean Let w1, w2, , wn denote the weights of the sample observations x1, x2, , xn such that w1 + w wn = 1, then Numerical Descriptive Measures

8
**Numerical Descriptive Measures**

3.2 Percentiles and Box Plots LO 3.2 Calculate and interpret percentiles and a box plot. In general, the pth percentile divides a data set into two parts: Approximately p percent of the observations have values less than the pth percentile; Approximately (100 p ) percent of the observations have values greater than the pth percentile. Numerical Descriptive Measures

9
**Numerical Descriptive Measures**

LO 3.2 Percentiles and Box Plots Calculating the pth percentile: First arrange the data in ascending order. Locate the position, Lp, of the pth percentile by using the formula: We use this position to find the percentile as shown next. Numerical Descriptive Measures

10
**Numerical Descriptive Measures**

LO 3.2 Percentiles and Box Plots Calculating the pth percentile Once you find Lp, observe whether or not it is an integer. If Lp is an integer, then the Lpth observation in the sorted data set is the pth percentile. If Lp is not an integer, then interpolate between two corresponding observations to approximate the pth percentile. Numerical Descriptive Measures

11
**Numerical Descriptive Measures**

LO 3.2 Percentiles and Box Plots Both L25 = 2.75 and L75 = 8.25 are not integers, thus The 25th percentile is located 75% of the distance between the second and third observations, and it is The 75th percentile is located 25% of the distance between the eighth and ninth observations, and it is Numerical Descriptive Measures

12
**Numerical Descriptive Measures**

LO 3.2 Percentiles and Box Plots A box plot allows you to: Graphically display the distribution of a data set. Compare two or more distributions. Identify outliers in a data set. Outliers Whiskers Box * * Numerical Descriptive Measures

13
**Numerical Descriptive Measures**

LO 3.2 Percentiles and Box Plots The box plot displays 5 summary values: Min = smallest value Max = largest value Q1 = first quartile = 25th percentile Q2 = median = second quartile = 50th percentile Q3 = third quartile = 75th percentile Min Max Numerical Descriptive Measures

14
**Numerical Descriptive Measures**

LO 3.2 Percentiles and Box Plots Detecting outliers Calculate IQR, then multiply by 1.5 There are outliers if Q1 – Smallest Value > 1.5*IQR Largest Value – Q3 > 1.5*IQR Numerical Descriptive Measures

15
**Numerical Descriptive Measures**

3.3 Measures of Dispersion LO 3.4 Calculate and interpret the range, the mean absolute deviation, the variance, the standard deviation, and the coefficient of variation. Measures of dispersion gauge the variability of a data set. Measures of dispersion include: Range Mean Absolute Deviation (MAD) Variance and Standard Deviation Coefficient of Variation (CV) Numerical Descriptive Measures

16
**Numerical Descriptive Measures**

LO 3.4 3.3 Measures of Dispersion Range It is the simplest measure. It is focused on extreme values. Numerical Descriptive Measures

17
**Numerical Descriptive Measures**

LO 3.4 3.3 Measures of Dispersion Mean Absolute Deviation (MAD) MAD is an average of the absolute difference of each observation from the mean. Numerical Descriptive Measures

18
**Numerical Descriptive Measures**

LO 3.4 3.3 Measures of Dispersion Variance and standard deviation For a given sample, For a given population, Numerical Descriptive Measures

19
**Numerical Descriptive Measures**

LO 3.4 3.3 Measures of Dispersion Coefficient of variation (CV) CV adjusts for differences in the magnitudes of the means. CV is unitless, allowing easy comparisons of mean-adjusted dispersion across different data sets. Numerical Descriptive Measures

20
**Numerical Descriptive Measures**

3.4 Mean-Variance Analysis and the Sharpe ratio LO 3.5 Explain mean-variance analysis and the Sharpe Ratio. Mean-variance analysis: The performance of an asset is measured by its rate of return. The rate of return may be evaluated in terms of its reward (mean) and risk (variance). Higher average returns are often associated with higher risk. The Sharpe ratio uses the mean and variance to evaluate risk. Numerical Descriptive Measures

21
**Numerical Descriptive Measures**

LO 3.4 3.4 Mean-Variance Analysis and the Sharpe Ratio Sharpe Ratio Measures the extra reward per unit of risk. For an investment І , the Sharpe ratio is computed as: where is the mean return for the investment is the mean return for a risk-free asset is the standard deviation for the investment Numerical Descriptive Measures

22
**Numerical Descriptive Measures**

3.5 Analysis of Relative Location LO 3.5 Apply Chebyshev’s Theorem and the empirical rule. Chebyshev’s Theorem For any data set, the proportion of observations that lie within k standard deviations from the mean is at least 11/k2 , where k is any number greater than 1. Consider a large lecture class with 280 students. The mean score on an exam is 74 with a standard deviation of 8. At least how many students scored within 58 and 90? With k = 2, we have 1(1/2)2 = At least 75% of 280 or 210 students scored within 58 and 90. Numerical Descriptive Measures

23
**Numerical Descriptive Measures**

LO 3.5 Analysis of Relative Location The Empirical Rule: Approximately 68% of all observations fall in the interval Approximately 95% of all observations fall in the interval Almost all observations fall in the interval Numerical Descriptive Measures

24
**Numerical Descriptive Measures**

LO 3.5 Analysis of Relative Location z-Scores Often useful to use z-score to denote relative location A sample value’s z-score indicates that sample value’s distance from the mean z-score calculated as A z-score of 1.25 indicates that that sample value is 1.25 standard deviations above the mean. A z-score of –2.43 would indicate a sample value that is 2.43 standard deviations below the mean. Numerical Descriptive Measures 24

25
**Numerical Descriptive Measures**

3.6 Summarizing Grouped Data LO 3.6 Calculate the mean and the variance for grouped data. When data are grouped or aggregated, we use these formulas: where mi and i are the midpoint and frequency of the ith class, respectively. Numerical Descriptive Measures

26
**Numerical Descriptive Measures**

3.7 Covariance and Correlation LO 3.7 Calculate and interpret the covariance and the correlation coefficient. The covariance (sxy or sxy) describes the direction of the linear relationship between two variables, x and y. The correlation coefficient (rxy or rxy) describes both the direction and strength of the relationship between x and y. Numerical Descriptive Measures

27
**Numerical Descriptive Measures**

LO 3.7 Covariance and Correlation The sample covariance sxy is computed as The population covariance sxy is computed as Numerical Descriptive Measures

28
**Numerical Descriptive Measures**

LO 3.7 Covariance and Correlation The sample correlation rxy is computed as The population correlation rxy is computed as Note, 1 < rxy < or 1 < rxy < +1 Numerical Descriptive Measures

Similar presentations

Presentation is loading. Please wait....

OK

Describing Data: Measures of Dispersion

Describing Data: Measures of Dispersion

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on save environment drawings Ppt on aerobics and fitness Ppt on school annual function Ppt on conservation of momentum example Ppt on object-oriented programming c++ Ppt on natural resources of pakistan Ppt on porter's five forces model template Mp ppt online form 2013 Ppt on anatomy of nervous system Ppt on pricing policy in business