Exercise 1. Using classical statistics describe the structure of monthly turnover of 80 shops [in tousand PLN].Calculate and interpret: arithmetic mean, standard deviation, coefficient of variability, coefficient of asymmetry (skewness) and coefficient of concentration (kurtosis).
Exercise 1. Using classical statistics describe the structure of monthly turnover of 80 shops [in tousand PLN].Calculate and interpret: arithmetic mean, standard deviation, coefficient of variability, coefficient of asymmetry (skewness) and coefficient of concentration (kurtosis).
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter10: Statistics
Section: Chapter Questions
Problem 25SGR
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Question
Exercise 1. Using classical statistics describe the structure of monthly turnover of 80 shops [in tousand PLN].Calculate and interpret: arithmetic mean, standard deviation, coefficient of variability, coefficient of asymmetry (skewness) and coefficient of concentration (kurtosis).
Exercise 2. Using positional statistics describe the structure of monthly turnover of 80 shops [in tousand PLN]Calculate and interpret:
Perform all calculations for the following data types:
- individual data,
- interval groupped data.
51 | 27 | 51 | 59 | 29 | 24 | 44 | 42 |
47 | 24 | 76 | 73 | 68 | 41 | 49 | 47 |
48 | 54 | 49 | 66 | 35 | 34 | 18 | 51 |
62 | 45 | 55 | 43 | 71 | 37 | 52 | 60 |
34 | 67 | 58 | 55 | 69 | 61 | 62 | 60 |
47 | 51 | 75 | 41 | 48 | 26 | 26 | 62 |
55 | 34 | 26 | 67 | 52 | 47 | 34 | 51 |
67 | 57 | 57 | 58 | 51 | 57 | 57 | 55 |
41 | 53 | 48 | 14 | 49 | 79 | 46 | 69 |
72 | 75 | 26 | 59 | 44 | 69 | 69 | 68 |
Exercise 3.
- Calculate Pearson's
correlation coefficient between expenditure (Y) and the income per 1 person (X). - Build a linear regression model describing the relationship between expenditure (Y) and income per 1 person (X):
- estimate the structural parameters of the regression model,
- interprete the regression coefficient,
- determine the theoretical values and residuals from the estimated model.
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