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![Question 1
[Soalan 1]
Based on the given multiple regression output tables below, answer the following
questions:
[Berdasarkan kepada jadual dapatan regresi berganda yang dinyatakan di bawah, jawab soalan-soalan
yang berikut:]
Model Summary
Adjusted R
Square
Std. Error of the
Durbin-
Model
R
R Square
Estimate
Watson
.887
.787
.776
0.29748
2.156
ΑNOVA
Sum of
Model
Squares
df
Mean Square
F
Sig.
Regression
18.347
3
6.116
69.107
.000
Residual
4.956
56
.088
Total
23.303
59
Coefficients
Standardized
Unstandardized Coefficients
Coefficients
Model
В
Std. Error
Beta
Sig.
1
(Constant)
-1.003
.315
-3.187
.002
X Variable 1
.325
.061
.350
5.307
.000
X Variable 2
.485
.069
.460
7.061
.000
X Variable 3
.457
.070
.428
6.556
.000](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fc65e72af-e768-4ed8-b0f5-1a9d5fb9cd56%2Fdff2d23b-c308-4532-815d-fd3875fca042%2Flas4174_processed.jpeg&w=3840&q=75)
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- Question #6 Listed below are altitudes (thousands of feet) and outside air temperatures (°F) recorded during a flight. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 95% confidence level with the altitude of 6327 ft (or 6.327 thousand feet). Altitude 4 8 14 24 27 31 32 Temperature 55 37 20 −5 −27 −41 −57 a. Find the explained variation. ______________ (Round to two decimal places as needed.) b. Find the unexplained variation. _______________ (Round to five decimal places as needed.) c. Find the indicated prediction interval. _____________°F < y < ____________ °F (Round to four decimal places as needed.)question 2 Shown below are the scatterplots and results of three linear regression analyses done that relate several different behavioural traits to one another in crabs, taken directly from a published paper. The dashed line in each is the line of best fit (least squares regression line). The fitted regression equation, r-value and p-value for each analysis are given in the figure legend below the graphs. Relationships among personality traits in the swimming crab Portunus trituberculatus. Each data point represents an individual crab; data are shown as the specific valye of personality traits (n=88). (A) Relationship between boldness and activity (y=36.04x-17.572, r=0.459, P=0.00). (B) Relationship between activity and hesitancy (y=-0.152x+3.37, r=0.305, P=0.00). (C) Relationship between boldness and hesitancy (y=-1.18x+7.07, r=0.158, P=0.02) For each graph (A – C) and the corresponding results in the figure caption above (A – C), comment on whether the result indicates the null…A researcher notes that, in a certain region, a disproportionate number of software millionaires were born around the year 1955. Is this a coincidence, or does birth year matter when gauging whether a software founder will besuccessful? The researcher investigated this question by analyzing the data shown in the accompanying table. Complete parts a through c below. a. Find the coefficient of determination for the simple linear regression model relating number (y) of software millionaire birthdays in a decade to total number (x) of births in the region. Interpret the result. The coefficient of determination is 1.___? (Round to three decimal places as needed.) This value indicates that 2.____ of the sample variation in the number of software millionaire birthdays is explained by the linear relationship with the total number of births in the region. (Round to one decimal place as needed.) b. Find the coefficient of determination for the simple linear regression model…
- Which of following is true or the most appropriate about a scatterplot with regression line or ordinary least square (OLS) regression? Question 18 options: No matter how you decide to draw your straight line, in general, all will will fall directly on the line. Any data point that does not fall directly on the line will have a certain amount of distance between the point and the line. If you were to calculate the distance between the data point and the line you have drawn, and then sum the distance, you would have a regression coefficient for the data points OLS is based on an idea that we have seen before: the sum of deviation.Which of following is true or the most appropriate about a scatterplot with regression line or ordinary least square (OLS) regression? Question 18 options: No matter how you decide to draw your straight line, in general, all will will fall directly on the line. Any data point that does not fall directly on the line will have a certain amount of distance between the point and the line. If you were to calculate the distance between the data point and the line you have drawn, and then sum the distance, you would have a regression coefficient for the data points OLS is based on an idea that we have seen before: the sum of deviation. Previous PageNext PageIf the statistical procedures covered in PSYC 2002 ultimately reduce to the GLM, why don't we just use the GLM? Question 1 options: none of these alternative are correct GLM is more complicated GLM is almost identical to multiple regression GLM is simpler
- Problem 3 addressed the cross-sectional variation in the number of financial analysts who follow a company. in that problem, company size and debt-to-equity ratios were the in- dependent variables. you receive a suggestion that membership in the S&P 500 index should be added to the model as a third independent variable; the hypothesis is that there is greater demand for analyst coverage for stocks included in the S&P 500 because of the widespread use of the S&P 500 as a benchmark. a. write a multiple regression equation to test whether analyst following is systematically higher for companies included in the S&P 500 index. also include company size and debt-to-equity ratio in this equation. use the notations below. (analyst following)i = natural log of (1 + number of analysts following company i) Sizei = natural log of the market capitalization of company i in millions of dollars (d/e)i = debt-to-equity ratio for company i S&Pi = inclusion of company i in the S&P…If i have cross sectional data for multiple years and plan to perform regression using panel techniques (pooled OLS, FE, or RE), which variable would serve as the cross section unit given the following variables (see below)? I believe it would be mode of transportation. If so, do i need to include the mode of transportation in my model as a regressor dummy variable for each mode? This is transportation data: year, mode of transportation (air, train, truck), country of origin, province of origin, country of destination, province of destination, commodities, shipments, revenue, weight.Following is a portion of the regression output for an application relating maintenance expense (dollars per month) to usage (hours per week) for a particular brand of computer terminal. question 12 attached in ss below thanks for elp appreiacted
- Section 10.3 Question #6 Listed below are altitudes (thousands of feet) and outside air temperatures (°F) recorded during a flight. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 95% confidence level with the altitude of 6327 ft (or 6.327 thousand feet). Altitude 4 11 14 20 28 31 34 Temperature 55 39 26 −2 −34 −41 −59 a. Find the explained variation. ______________ (Round to two decimal places as needed.) b. Find the unexplained variation. _______________ (Round to five decimal places as needed.) c. Find the indicated prediction interval. _____________°F < y < ____________ °F (Round to four decimal places as needed.)QUESTION 19 STAATS The following results are from data concerning the amount withdrawn from an ATM machine based on the amount of time spent at the ATM machine (SECONDS) and the gender, FEMALE (dummy variable = 1 for females and = 0 for males) and an interaction term, SECONDS*FEMALE SUMMARY OUTPUT Regression Statistics Multiple R 0.503 R Square Adjusted R Square Standard Error Observations 50 ANOVA df SS MS F Significance F Regression 2728.7 5.19 0.004 Residual 24161.8 525.3 Total 32348 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 134.3…Question 20 On the basis of data provided by a Romac Salary survey, the variance in annual salaries for seniors in public accounting firms is approximately 2.1 and the variance in annual salaries for managers in public accounting firms is approximately 11.1. The salary data were provided in thousands of dollars. Assuming that the salary data were based on samples of 25 seniors and 26 managers, test the hypothesis that the population variances in the salaries are equal. At a 0.05 level f significance, what is your conclusion?
