In: Statistics and Probability
Scenario: | |||||||
The researhers want to know if there is a relationship between the number of | |||||||
cars sold and revenues in hopes of developing a predictive model in the near | |||||||
future. Below is the number of cars sold and revenue generated for 6 automotive | |||||||
dealerships. You're expected to create a SCATTERPLOT diagram, then insert a line of | |||||||
best fit and the regression equation. | |||||||
Company | Cars (in ten thousands) | Revenue (in billions) | |||||
A | 63 | 7 | |||||
B | 29 | 3.9 | |||||
C | 20.8 | 2.1 | |||||
D | 19.1 | 2.8 | |||||
E | 13.4 | 1.4 | |||||
F | 8.5 | 1.5 | |||||
Please provide your interpretation of results. For example, you might want to describe the direction of | |||||||
the relationship between the X and Y variables. You may also want to share your thoughts regarding | |||||||
the strength of the relationship. This will be reflected in how closely the data points cluster around the | |||||||
line of best fit. Finally, you would probably want to make reference to the r2, and narratively interpret | |||||||
this rate. You may also want to share some relevant descritpive statistics on the cars and revenues. |
yes car(in ten thousand) and revenue are possitively correlated
Correlations
cars(in ten thousand)
and Revenue(in billion) Pearson Correlation is 0
.982
The most of the point are near to straight line so model is normally disrtibuted
the R sqaure value is 0.9643 means the model is good fit
descriptive statistics
Call:
lm(formula = y ~ x)
Residuals:
1 2 3 4 5 6
-0.08222 0.42604 -0.50373 0.37669 -0.41840 0.20161
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.39632 0.31913 1.242 0.282117
x 0.10613 0.01021 10.392 0.000484 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4471 on 4 degrees of freedom
Multiple R-squared: 0.9643, Adjusted R-squared: 0.9554
F-statistic: 108 on 1 and 4 DF, p-value: 0.0004842