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Previous1/217) Sarah is the oflice manager for group of financial advisors who provide financial services for (ISpts) individual clients. She would like to investig...

Question

Previous1/217) Sarah is the oflice manager for group of financial advisors who provide financial services for (ISpts) individual clients. She would like to investigate whether relationship exists between the number of presentations made to prospective clients in month and the number of new clients per month: The following table shows the number of presentations and corresponding new clients for & random sample of six employees.PresentationsNew ClientsEmployec Employee Employce Employee = Emp

Previous 1/2 17) Sarah is the oflice manager for group of financial advisors who provide financial services for (ISpts) individual clients. She would like to investigate whether relationship exists between the number of presentations made to prospective clients in month and the number of new clients per month: The following table shows the number of presentations and corresponding new clients for & random sample of six employees. Presentations New Clients Employec Employee Employce Employee = Employce Employce Regression Equation= Sarah would like t0 usc simple regression analysis t0 estimate the number of new clients per month bascd on the number Of presentations made by the cmployce per month: The regression equation What your conclusion about the relationship between the amount of presentations and the number Of new clients? Use value and the 95%0 contidence interval; Quality of Model: State your conclusions about the strength Of model overall using the _ Lesl and thc R ? tlue Predicted Value of Y: If 8 presentations Were piven; what the expected amount 0f new clients (ie: Yhat) ? What is the 95% confidence and prediction interval for yhat? Use the values in & complete sentence



Answers

A regression of averrage weekly earnings (AWE, measured in
dollars) on age (measured in years) using a random sample of college-educated
full-time workers aged 25-65 yields the following:
AW Eˆ = 689.88 + 10.67 × Age, R2 = 0.045
(4 points) (i) Explain what the coecient values 689.99 and 10.67 mean.
(2 points) (ii) The regression R2
is 0.045. What are the units of measurement
for the R2
? (Dollars? Years? Or is R2unit-free?)
(2 points) (iii) What is the regression's predicted earnings for a 28-year-old
worker? A 38-year-old worker?
(4 points) (iv) Will the regression give reliable predictions for a 95-year-old
worker? Why or why not?
(2 points) (v) The average age in this sample is 41 years. What is the average
value of AWE in the sample?

In this question. Well, equal Negative. 14.9 bus 0.9 if the like nine x one thus 0.99847 next door. Thus, 5.38 for four. Next by replacing X one with yeah, extra. Thank you. For, um, 63 living we could well equals negative. 14.9 less 0.9. Seriously, life nine. My complete boy. Okay, but point 99 here for seven, but played by it before Those five point support for Might have laid by the living equals he 85 point 74 play for for approximately you Here. Thank you.

Alright. Uh This question it says a professor in the school of business in a university poll, a dozen colleagues about the number of professional meetings professors attend In the past attended in the past five years is exceeded. And the number of papers submitted by those to refer to generals by those who referred journals, it's widely during the same period. Okay. Now in the question there is a summer of data that is given. We have to derive a formula day from the somebody that is given in this question. So the question says fit a simple linear regression model between X and Y by finding out these estimates of intercept and slope, then comment whether attending professional meetings would result in publishing more purpose. Oh okay. Right. A Good one There. So now after analyzing that I realized that from the data summary data summary that is given in the question we can obtain the following. So they've given an X. Bar. The experts are the mean that we cannot fly by 12 because our end is 12 days. So if you multiply four by 12, you get 48, 12 by 12, you get 1 44. Then this is given. And this one also. Right. And this one, if you square 48 you get 230 four. There Okay, so now from there we can now derive our linear regression care of the online. Right? So right. With this linear question that I've given their my a pa is the slope because you're supposed to get the slope and the intercept. So this is my slop. Right? Morality is low. Right? So if you plug in values. Uh Right. If you block in the values there you get miners six 045. So the slope is is minus uh six Coma 4 5. Then the intercept this is the intercept is the intercept there. Okay, that's the interstate. Alright. So to get the intercept, you just plug in the values with their lives And you get the intercept is 37 Kumar eight days. So the linear regression equation that can be obtained there. This is the linear equation. Why? by supposed to 37 8 -6 045 x 10. And they said you should comment, give a comment. Okay. Um let's take it this way. If our X. Is zero, what is R Y pa Our body is 37 eight. But let's increase X. Now let's say X is equal to taint. You want to see if y part is going to increase, what's going to decrease. So wipe are there, it is going to decrease because 10 times 6:04 It becomes 64 37 -64. It's a negative. So it's when one is increasing this one is decreasing. So there's so we are saying it appears that attending professional meetings will not result in publishing more papers. I think cause this, you can see the slope is negative there. If the slope was positive, we can say yes, attending professional meetings would also result in publishing more papers. But now the slope is negative. When one increases, the other one is decreasing. Okay? Thank you.

We're giving to the questions. The estimator is in data in the milk on single on. The first question basically wants us say, if your policy makers, trying to estimate the cultural effects off our students spending on March test for four months, explain why the first equation is more advanced than the second. I was estimated effect over 10% increase in expenditures per student. So the parts one off the question. In the first regression model, the first regression model for Vex P P p. We have 9.1 is the divided by 4.4 and just give you two points. 23. Now the second regression model. The test statistics for home for this will be close to 1.93 divided by two points 82 just give you 0.68 So as the L E X P T is the key explanatory variable and its significance in the first model on not significant in the second model would keep using the first model. So would use the first Modell because that's significant, and it's so for 1% increase. I'm sorry. 10% increase in the legs p p p. There's an average off increase off. There's an increase on does, on average increase off 0.901 in the math test in matters performance. So a old other predictors fixed. So now the path to off the question, um, does reading the read four to the real question have strange effects on coefficients as that skull significance other than be legs p p. P. So we observed that reading after reading for in the first regression model, we'll make on the adding with for Radha. Yeah, I didn't read for in the first reduction model who make the standard error off free investing on to to decrease. So Adan read four to the first regression model. We'll make the standard error to make a stunning era off free coma. That's and this should decrease on the I coughed I inflation of coefficients off this. So that is the next question. How do you explain to someone with only basic knowledge of regression why, in this case, you prefer the question the smaller adjusted are square? Um, I will explain that we have preferred this, we question with the smaller adjusted are square because when in that question with the key exponent, tree variable is significant. So we prefer the question with smaller. I just did our square because the key explanatory variable a significance in the regression model, so that's your

Well I think the question that's for the move to dance or so. Let's see here that the first point is coefficient value 689 points. It did is the constant, it only tells us the intercept of the recreation model. When we ignore edge, we can tell that the average weekly earnings is predicted to the 689 Point $88. So the coefficient of age 10.67 means that With and one year increasing age die very quickly income on earning increase By $10.67. It is a slope of deregulation model and the second point is our two majors goodness of fake higher arguments. Our explanatory variables fit that role model better. R. two is unitary and life between zero and one so therefore are too is unitary. And the third point is predict earning of 28 year old workers is so A W. A. Is equal to dollars. 689 points 88 plus 10.67 bracket 28 which is equal to 689.88 Plus 298 76. Which is equal to 988 64. So predict earning for a 38 year old worker is W. E. Is equal to 689 points 88 plus 10.67 bracket 30 years is equal to 689.88 Plus 405 1 46 is equal to 1095 points. $34. and the 4th point is no this is because here the sample include worker between edge 25 to 65. So let's move to the 51. If the average age in the sample is 41 then the red value of W. E. In the same place. So A. W. A. Is equal to 689.88 plus 10.67 bracket 41 is equal to 689.88 plus 437 points 47 is equal to 1127 points $35. So here is all the five points which are given in the question that for the first point is coefficient value 689.13. It is the constant. Um if only tells us that intercept of the regulation model. When we ignore it, we can tell that the average weekly earnings predicted to be $689.88. So the coefficient of age 10.67 means that within one year increasing age the average weekly income unearthing increased by $10.67 is the slope of the decoration model and the second point is our two major goodness of fit. Higher arguments are explanatory variables for the true model, better are too is unit free and lies between zero and one. So therefore are two is the unit free and the third point is credit earning for a 28 year old worker, is A W. E. Dollars, 689.88 plus 10.67 Bracket 28 is equal $2, plus 298.76 is equal to 988.64. And the predicted earning for a 38 years old worker is a. W. Is A. W. E. Is equal $2 689.88 plus 10.67 bracket 38 is equal $2 689.88 plus 5, 405.46 Which is equal to $1,095.34. And the fourth point is notices because there's a sampling clears worker between age 25 to 65 the fifth point is, if the averages age in the sample is 41 than the average value of A. W. E. Is in the sample is A. W. E. is equal $2, plus. 10.67 Bracket 41 is equal to 689.38 plus 437.47 is equal to $1,127.35. I hope you better understand all the five points. So for any further curious and doubts I'm there for help. So thank you


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