Just as you are about to estimate a regression, massive sunspots cause magnetic interference that ruin all electrically powered machines (e.g., computers). Instead of giving up and flunking, you decided to calculate estimates from your data (on per capita income in thousands of U.S. dollars as a function of the percent of labor force in agriculture in 10 developed countries) manually. Your data are given below. Country % in Agriculture Per Capita Income A 9 6 B 10 8 C 8 8 D 7 7 E 10 7 F 4 12 G 5 9 H 5 8 I 6 9 J 7 10 Do a scatter plot between % in Agriculture and Per Capita Income using Excel. Place % in Agriculture on the X-axis. Copy and paste the scatter plot in the space below. Comment on the nature of the relationship between % in Agriculture and Per Capita income. Note: Use the numbers in the data as given. Do not convert those to percentage. Use Excel to compute Covariance and Correlation Coefficient. Copy and paste the Excel calculations in the space below. Note: Use % in Agriculture as X-variable and Per Capita Income as Y-variable. Comment on the nature of the relationship between % in Agriculture and Per Capita Income based on the value of the correlation coefficient computed in question (b).
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.
Just as you are about to estimate a regression, massive sunspots cause magnetic interference that ruin all electrically powered machines (e.g., computers). Instead of giving up and flunking, you decided to calculate estimates from your data (on per capita income in thousands of U.S. dollars as a
|
Country |
% in Agriculture |
Per Capita Income |
|
A |
9 |
6 |
|
B |
10 |
8 |
|
C |
8 |
8 |
|
D |
7 |
7 |
|
E |
10 |
7 |
|
F |
4 |
12 |
|
G |
5 |
9 |
|
H |
5 |
8 |
|
I |
6 |
9 |
|
J |
7 |
10 |
- Do a
scatter plot between % in Agriculture and Per Capita Income using Excel. Place % in Agriculture on the X-axis. Copy and paste the scatter plot in the space below. Comment on the nature of the relationship between % in Agriculture and Per Capita income. Note: Use the numbers in the data as given. Do not convert those to percentage. - Use Excel to compute
Covariance andCorrelation Coefficient . Copy and paste the Excel calculations in the space below. Note: Use % in Agriculture as X-variable and Per Capita Income as Y-variable. - Comment on the nature of the relationship between % in Agriculture and Per Capita Income based on the value of the correlation coefficient computed in question (b).
- Visit Spurious
Correlations http://tylervigen.com/spurious-correlations. Choose any 2 variables that are highly correlated (r2>.80). Recreate the data set and run the correlation. Copy your results here. Can you provide any explanation for these results? Pick a third data set and provide the correlation of this data with your original 2. Copy those results here.
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