What is the estimated percentage difference in the number of awards between a student in the academic program and a student in the vocational program, holding other factor
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What is the estimated percentage difference in the number of awards between a student in the academic program and a student in the vocational program, holding other factors fixed?
![poisson num_awards math i.prog
Iteration 0: log likelihood = -182.75759
Iteration 1: log likelihood = -182.75225
Iteration 2: log likelihood = -182.75225
Poisson regression
Log likelihood = -182.75225
num_awards
math
prog
academic
vocation
cons
Coefficient
. 0701524
1.083859
.3698092
Std. err.
. 0105992
.358253
.4410703
-5.247124 .6584531
Z
P> |Z|
6.62 0.000
3.03 0.002
0.84 0.402
-7.97 0.000
Number of obs =
LR chi2 (3)
Prob> chi2
Pseudo R2
0493783
[95% conf. interval]
.3816962
-.4946727
200
98.22
= 0.0000
= 0.2118
-6.537669
=
0909265
1.786022
1.234291
-3.95658](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F295fd57e-125d-4c83-8c30-33e119b97b32%2F5328af75-4fc0-41f6-a08f-fd4e6c0db3d6%2F6eolldi_processed.png&w=3840&q=75)
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- please anwer whatever you are allowed too. Thank you. A regression was run to determine if there is a relationship between the happiness index (y) and life expectancy in years of a given country (x).The results of the regression were: ˆyy^=a+bxa=-1.68b=0.168 (a) Write the equation of the Least Squares Regression line of the formˆyy^= + x(b) Which is a possible value for the correlation coefficient, rr? -1.417 1.417 0.702 -0.702 (c) If a country increases its life expectancy, the happiness index will increase decrease (d) If the life expectancy is increased by 0.5 years in a certain country, how much will the happiness index change? Round to two decimal places.(e) Use the regression line to predict the happiness index of a country with a life expectancy of 69 years. Round to two decimal places.Please help me to write the interpretation for ANOVA and linear regression for both countries in Austria and United Kingdom (Attachment is there)The Average final score was 78, the average midterm score was 80. The standard deviation for the average final midterm score was 4.5 and the standard deviation for the average midterm score was 5.5. The correlation coefficient is 0.76 Find the least squares regression line. Supposed that wanted to find to predict the final exam score based the midterm score
- The data show the chest sire and weight of several bears Find the regression equation, leting chest size be the independent(x)variable. Then find the best predicted we ght of a bear inith a chest sizo of 40 inches. Is the result close to the actual weight of 272 pounds? Use a signifcance level of 0.05 8 Click the icon to verw the critical values of the Pearson correlation coefficient . What is the regrestion equation? \[ \hat{y}=\text {. K(Round ta one decimal place as needed) } \]Use the least squares regression line of this data set to predict a value. Meteorologists in a seaside town wanted to understand how their annual rainfall is affected by the temperature of coastal waters. For the past few years, they monitored the average temperature of coastal waters (in Celsius), x, as well as the annual rainfall (in millimetres), y. Rainfall statistics • The mean of the x-values is 11.503. • The mean of the y-values is 366.637. • The sample standard deviation of the x-values is 4.900. • The sample standard deviation of the y-values is 44.387. • The correlation coefficient of the data set is 0.896. The least squares regression line of this data set is: y = 8.116x + 273.273 How much rainfall does this line predict in a year if the average temperature of coastal waters is 15 degrees Celsius? Round your answer to the nearest integer. millimetresFind the slope of regression line, y-intercept of regression line, coefficient of determination (r^2), and the linear correlation coefficient (r)
- Does the Regression line give information about all the data points in the data set? Does the Regression line usually have all the points in the data set on it?Using SAS, draw a scatterplot between variables CRIME_RATE and PROP_CHANGE_INCOME. Attach the scatterplot. Are those two variables good candidates to be analyzed using linear regression? Explain why or why not. crime_rate 150 100 50 O 15 O O 20 O O O 25 O 8 O O 8 O O o O prop_change_income O O O 30 O O O 35 O O O 40call: Researchers measured the percent 1m(formula = Symptoms - wear_mask, data - some_states) of people in 25 states who ʻknew someone with COVID symptoms' (ŷ) and regressed this on the percent of the population frequently wearing a mask in public (x). Residuals: Min -7.9167 -2.3306 -0.2469 2.5020 7. 3345 10 Median 30 Маx coefficients: (Intercept) 111.0981 wear_mask Estimate std. Error t value Pr (>|t|) 10. 5423 10. 538 2.82e-10 *** -8. 375 1. 94 e-08 *** -1.0419 0.1244 signif. codes: 0 ****' 0.001 ***' 0.01 **' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.859 on 23 degrees of freedom Multiple R-squared: 0.7531, F-statistic: 70.15 on 1 and 23 DF, p-value: 1.936e-08 Adjusted R-squared: 0.7423 If 75 percent of people in a state wear masks regularly, what % of people does this model predict will know someone with COVID symptons? 1) 32 2) 33 3) 34 4) 35
- how to plot the estimated regression line on the scatter diagram in letter c and how to solve for letter d with graphInterpret the least squares regression line of this data set. Meteorologists in a seaside town wanted to understand how their annual rainfall is affected by the temperature of coastal waters. For the past few years, they monitored the average temperature of coastal waters (in Celsius), x, as well as the annual rainfall (in millimetres), y. Rainfall statistics • The mean of the x-values is 11.503. • The mean of the y-values is 366.637. • The sample standard deviation of the x-values is 4.900. • The sample standard deviation of the y-values is 44.387. • The correlation coefficient of the data set is 0.896. The correct least squares regression line for the data set is: y = 8.116x + 273.273 Use it to complete the following sentence: The least squares regression line predicts an additional annual rainfall if the average temperature of coastal waters increases by one degree millimetres of Celsius.Define regression line