I have one problem I am stuck on, please anyone?
here is my data

Simple linear regression results:
Dependent Variable: weight
Independent Variable: length
weight = -491.00992 + 28.526619 length
Sample size: 158
R (correlation coefficient) = 0.9245
R-sq = 0.8546454
Estimate of error standard deviation: 137.34116

Parameter Estimate Std. Err. DF T -Stat P-Value
Intercept -4 91.00992 31.343002 156 -15.665696 <0.0001
Slope 28.526619 0.94191015 156 30.285923 <0.0001

Analysis of variance table for regression model:
Source DF SS MS F-stat P-value
Model 1 1.730147E7 1.730147E7 917.2371 <0.0001
Error 156 2942564.5 18862.594
Total 157 2.0244036E7

Predicted values:
X value Pred. Y s.e.(Pred. y) 95% C.I . 95% P.I.
36.5 550.2116 12.017157 (526.4743, 573.949) (277.88684, 822.53644)







I need to:
write a comment about the strength of the relationship between the 2 variables based on the correlation coefficient.
write the linear regression equation for the data
what does the linear regression predict the weight of a fish should be when the length is 36.5 cm?

thanks !

The correlation coefficient is 0.9245, so what does that suggest about the strength and type of correlation between the two variables?

It looks like you've already come up with the regression equation:
weight = -491.00992 + 28.526619 length

So now, simply plug in 36.5 cm for length and calculate the predicted weight

Scenario: Regression equations are created by modeling data, such as the following:

Profit = (Cost Per Item × Number of Items) – Constant Charges

In this equation, constant charges may be rent, salaries, or other fixed costs. This includes anything that you have to pay for periodically as a business owner. This value is negative because this cost must be paid each period and must be paid whether you make a sale or not.

Your company may wish to release a new e-reader device. Based on data collected from various sources, your company has come up with the following regression equation for the profit of the new e-reader:

Profit = $0.15 × number of e-readers sold – $28

Or, assuming x = the number of e-readers sold, this would be the same regression equation:

Profit = 0.15x – 28



In this case, the values are given in thousands (i.e. the cost of making an individual e-reader will be $150 [0.15 × 1,000], with $28,000 [28 * 1,000] in constant charges).
Answer the following questions based on the given regression equation:

1.Using the graphing program that you downloaded, graph the profit equation. Discuss the meaning of the x- and y-axis values on the graph. (Hint: Be sure to label the axis)
2.Discuss the meaning of the slope of the equation that you have just graphed. How is it related to the cost of each e-reader?
3.Based on the results of the graph and the profit equation provided, discuss the relationship between profits and number of e-readers produced. (Hint: Consider the slope and y-intercept.)
4.If the company does not sell a single e-reader, how much is lost ? Mathematically, what is this value called in the equation?
5.If the company sells 5,000 e-readers, how much will the company make (or lose)?
6.If profit must equal 100 thousand, how many e-readers will your company need to sell? (Round up to the nearest e-reader.)
7.If your company is hoping to break even, how many e-readers will need to be sold to accomplish this? (Round up to the nearest e-reader.)

Please show me the work and answer to this problem