The researcher wishes to use numerical descriptive measures to summarize the data on each of the two variables: hours worked per week and yearly income. a) Prepare and display a numerical summary report for each of the two variables including summary measures such as mean, median, range, variance, standard deviation, smallest and largest values and the three quartiles. Notes: Use QUARTILE.EXC command to generate the three quartiles. b) Compute the correlation coefficient using the relevant Excel function to measure the direction and strength of the linear relationship between the two variables. Display and interpret the correlation value. Hours Per Week Yearly Income ('000's) 18 43.8 13 44.5 18 44.8 25.5 46.0 11.5 41.2 18 43.3 16 43.6 27 46.2 27.5 46.8 30.5 48.2 24.5 49.3 32.5 53.8 25 53.9 23.5 54.2 30.5 50.5 27.5 51.2 28 51.5 26 52.6 25.5 52.8 26.5 52.9 33 49.5 15 49.8 27.5 50.3 36 54.3 27 55.1 34.5 55.3 39 61.7 37 62.3 31.5 63.4 37 63.7 24.5 55.5 28 55.6 19 55.7 38.5 58.2 37.5 58.3 18.5 58.4 32 59.2 35 59.3 36 59.4 39 60.5 24.5 56.7 26 57.8 38 63.8 44.5 64.2 34.5 55.8 34.5 56.2 40 64.3 41.5 64.5 34.5 64.7 42.3 66.1 34.5 72.3 28 73.2 38 74.2 31.5 68.5 36 69.7 37.5 71.2 22 66.3 33.5 66.5 37 66.7 43.5 74.8 20 62.0 35 57.3 24 55.3 20 56.1 41 61.5
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
The researcher wishes to use numerical descriptive measures to summarize the data on each of the two variables: hours worked per week and yearly income.
a) Prepare and display a numerical summary report for each of the two variables including summary measures such as mean,
Notes: Use QUARTILE.EXC command to generate the three quartiles.
b) Compute the
| Hours Per Week | Yearly Income ('000's) |
| 18 | 43.8 |
| 13 | 44.5 |
| 18 | 44.8 |
| 25.5 | 46.0 |
| 11.5 | 41.2 |
| 18 | 43.3 |
| 16 | 43.6 |
| 27 | 46.2 |
| 27.5 | 46.8 |
| 30.5 | 48.2 |
| 24.5 | 49.3 |
| 32.5 | 53.8 |
| 25 | 53.9 |
| 23.5 | 54.2 |
| 30.5 | 50.5 |
| 27.5 | 51.2 |
| 28 | 51.5 |
| 26 | 52.6 |
| 25.5 | 52.8 |
| 26.5 | 52.9 |
| 33 | 49.5 |
| 15 | 49.8 |
| 27.5 | 50.3 |
| 36 | 54.3 |
| 27 | 55.1 |
| 34.5 | 55.3 |
| 39 | 61.7 |
| 37 | 62.3 |
| 31.5 | 63.4 |
| 37 | 63.7 |
| 24.5 | 55.5 |
| 28 | 55.6 |
| 19 | 55.7 |
| 38.5 | 58.2 |
| 37.5 | 58.3 |
| 18.5 | 58.4 |
| 32 | 59.2 |
| 35 | 59.3 |
| 36 | 59.4 |
| 39 | 60.5 |
| 24.5 | 56.7 |
| 26 | 57.8 |
| 38 | 63.8 |
| 44.5 | 64.2 |
| 34.5 | 55.8 |
| 34.5 | 56.2 |
| 40 | 64.3 |
| 41.5 | 64.5 |
| 34.5 | 64.7 |
| 42.3 | 66.1 |
| 34.5 | 72.3 |
| 28 | 73.2 |
| 38 | 74.2 |
| 31.5 | 68.5 |
| 36 | 69.7 |
| 37.5 | 71.2 |
| 22 | 66.3 |
| 33.5 | 66.5 |
| 37 | 66.7 |
| 43.5 | 74.8 |
| 20 | 62.0 |
| 35 | 57.3 |
| 24 | 55.3 |
| 20 | 56.1 |
| 41 | 61.5 |
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