Q write 3 questions about linear Regression and 2 questions

Q- write 3 questions about linear Regression, and 2 questions about python scripting language. please provide 7 answer options for EACH Question and the select answer for EACH question .

note :these questions is in the artificial intelligence

Solution

Regression Equation Section

Independent     Regression     Standard                  T-Value                Prob                  

Variable               Coefficient     Error                          (Ho: B=0)             Level                                            

INTERCEPT       -7.964633                3.11101359       -2.560                       0.0166

X1                           12.548580            1.27081204        9.874                        0.0001

1. What statistical conclusion should you make about the effect of the dividend on average stock price?

A. Since 11.30869 > table value, reject the null hypothesis.

B. Since 12.54858 > table value, reject the null hypothesis.

C. Since 9.874 < table value, reject the null hypothesis.

D. Since 9.874 > table value, reject the null hypothesis.

E. Since 0.7895 < table value, fail to reject the null hypothesis.

2. What is the 95% confidence interval for a value of Y given an X value of 2.36? You are given the standard error of this estimate is 3.351

1) in the sample is interpreted as: I am 95% confident that

A. the stock price for a stock with a dividend rate of 2.36% falls between $14.61 and $28.69.

B. the mean stock price for all stocks with a dividend rate of 2.36% falls between $14.61 and $28.69.

C. the variance in stock price for all stocks falls between $14.61 and $28.69.

D. the dividend rate for all stocks falls between $14.61 and $28.69.

E. for each one point increase in dividend rate, the stock price will increase from $14.61 and $28.69

3. Which one of the following assumptions is incorrectly stated?

A. The stock price is normally distributed for any dividend rate.

B. The stock price has the same variability for any dividend rate.

C. The stock price for any dividend rate is a linear function of dividend rate.

D. The difference between the stock price and the expected stock price

given the dividend rate is independent from company to company.

4. The interpretation of 0.7895, the value of R-square (the coefficient of determination) is:

A. 78.95% of the sample stock prices (around the mean stock price) can be attributed to a linear relationship with the dividend rate in the population.

B. the mean stock price will be estimated to increase $97.50 for each point increase in the rate.

C. the mean stock price will be increase $78.95 for each point increase in the rate.

D. the stock price will increase $78.95 for each point increase in the rate.

E. 78.95% of the sample variability in stock price (around the mean stock price) can be attributed to a linear relationship with the dividend rate.

5. What is the estimate of the change in expected stock prices when the dividend rate increases by one point?

A. 97.50

B. -7.964633

C. This is a parameter not a statistic.

D. 12.54858

E. 5.36546

6. The estimate of the slope will vary from sample to sample, the estimate of the standard deviation of beta-hat is:

A. 3.36284

B. 3.14983

C. 0.39274

D. 12.54858

E. 1.27081

7. A 95% confidence interval for the average stock price given the rate of return will use the following t value:

A. 9.874

B. -2.560

C. 2.101

D. 2.045

E. 2.153

Answers to 1-7

1. D from computer printout use the t-test value across from X1  

2. A this is a confidence interval for a conditional mean  

3. C the mean stock price falls on the line   

4. E r-square is % of sample variation of y explained by x   

5. D This is beta-hat – see computer printout to the right of X1  

6. E This is the standard error of hat to right of X1

7. C All t-values in simple linear regression have n-2 d. f.

Why Is <__init__.Py> Module Used In Python?

The <__init__.py> module can help in fulfilling following objectives.

It makes Python interpret directories as containing packages by excluding the ones with a common name such as string.It grants a programmer with the control to decide which directory is a package and which is not. However, the <__init__.py> can also be an empty file. It can then help in executing the initialization code for a package or setting the <__all__> variable.

What Is Pickling And How Does It Different From Unpickling?

Pickling is a process by which a Python object get converted into a string via a pickle module. The process then puts it into a file by calling the dump() method.Whereas unpickling does the reverse of the above-said process. It retrieves the stored string and turns it back into an object.