You are interested in the prediction of apartment rent from square footage. You sample 25 apartments in the Glendale area and record the apartment rent costs and square footage. When you put this into Excel, you get the following output:
Multiple R 0.850060796
R Square 0.722603356
Adjusted R Square 0.710542633
Standard Error 194.5953946
df SS MS F Significance F
Regression 1 2268776.545 2268776.545 59.91376452 7.51833E-08
Residual 23 870949.4547 37867.3676
Total 24 3139726
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 177.1208202 161.0042766 1.100100096 0.282669853 -155.9414484 510.1830889
Size 1.065143906 0.137608412 7.740398215 7.51833E-08 0.780479605 1.349808208
1. What is the regression equation for these data?
2. How much of the variance in rent can be predicted by knowing the square footage?
3. Is this a significant regression equation?
4. What does this mean?
Wages in the construction industry average $11.90 per hour with a standard deviation of $1.40.
5. What percent of workers make less than $10.50 per hour (z = -1.00)?
6. What percent of workers make more than $15 per hour (z = 2.21)?
This solution involves various questions regarding a regression analysis.
Regression : key information
1. List one unclear concept regarding regression and provide an example from your personal or work life where regression could be used to show a relationship between two variables.
2. Correlation does not equal causation. That is very important to understand when interpreting regression. Can anyone elaborate for me?(100+ words please)
(ii) One should never use a regression line to predict the dependent response variable when the independent value is outside of the data range of the original data set that was used to fit the line. Explain why.(100+ words Please)View Full Posting Details