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I have no idea how to fill in the blank in this regression output... please help

Interpreting Simple Linear Regression 1. Linear correlation coefficient r = 0.794556 2. Coefficient of Determination ( r square) 0.631319 3. Standard Error of the estimate = 12.9668 4. SSR (Explained variation) = 5182.41 5. SSE ( Unexplained variation) = 3026.49 6. Predicted equation or equation of the regression line (Y predicted or hat) = 0.725983X + 16.5523 7. Level of Significance = 0.1 8. Critical Value= 0.378419   Question : Based on the linear correlation coefficient (r) in Line 1, the variables(X,Y) are positively correlated   True False

Interpreting Simple Linear Regression 1. Linear correlation coefficient r = 0.794556 2. Coefficient of Determination ( r square) 0.631319 3. Standard Error of the estimate = 12.9668 4. SSR (Explained variation) = 5182.41 5. SSE ( Unexplained variation) = 3026.49 6. SST = 8208.90 7. Predicted equation or equation of the regression line (Y predicted or hat) = 0.725983X + 16.5523 8. Level of Significance = 0.1 9. Critical Value= 0.378419 Question What is the meant by the standard error of the estimate? Which number measures the scatter of points about the regression line?

Interpreting Simple Linear Regression 1. Linear correlation coefficient r = 0.794556 2. Coefficient of Determination ( r square) 0.631319 3. Standard Error of the estimate = 12.9668 4. SSR (Explained variation) = 5182.41 5. SSE ( Unexplained variation) = 3026.49 6. SST = 8208.90 7. Predicted equation or equation of the regression line (Y predicted or hat) = 0.725983X + 16.5523 8. Level of Significance = 0.1 9. Critical Value= 0.378419 10. t test = 0.794556 Question What is meant by a hypothesis? State the hypothesis in this example?   A hypothesis is a claim about the correlation between the Y and X variables in the population under study: There are two hypothesis (claims) Null Hypothesis is that (rho) = 0 Alternative Hypothesis is (rho) is not equal to 0   A hypothesis is a claim about the correlation between the Y and X variables in the population under study: There is only one hypothesis (claim) as follows: Null Hypothesis is that (rho) = 0   A…

Interpreting Simple Linear Regression 1. Linear correlation coefficient r = 0.794556 2. Coefficient of Determination ( r square) 0.631319 3. Standard Error of the estimate = 12.9668 4. SSR (Explained variation) = 5182.41 5. SSE ( Unexplained variation) = 3026.49 6. SST = 8208.90 7. Predicted equation or equation of the regression line (Y predicted or hat) = 0.725983X + 16.5523 8. Level of Significance = 0.1 9. Critical Value= 0.378419 Question What is meant by SSR? Which number measures the variation explained by the regression line?   Sum of the Squares Regression (SSR); amount of variation in Y explained by the variation in X explanatory variable   Sum of the Squares Regression (SSR); amount of variation in X explained by the variation in Y   SSR is the percent variation in total variation SST that is explained by the variation in X or SSR/SST = 5182.41/8208.90 = 0.631319 or 63%   Both A and C

Interpreting Simple Linear Regression 1. Linear correlation coefficient r = 0.794556 2. Coefficient of Determination ( r square) 0.631319 3. Standard Error of the estimate = 12.9668 4. SSR (Explained variation) = 5182.41 5. SSE ( Unexplained variation) = 3026.49 6. SST = 8208.90 7. Predicted equation or equation of the regression line (Y predicted or hat) = 0.725983X + 16.5523 8. Level of Significance = 0.1 9. Critical Value= 0.378419 10. t test = 0.794556 Question What is meant by a hypothesis? State the hypothesis in this example? A hypothesis is a claim about the correlation between the Y and X variables in the population

Why is the equation of the regression line for this scatter plot ?

В. A model estimated using a dataset with 125 observations generates the following results. SS df MS Regression 919587.543 4 229896.9 Error 2590390.62 121 534.2113 Std. Variable B Error t P>lt| X2 -0.0126355 0.005519 -2.28937 0.022 X3 0.5957923 0.014482 41.13934 0.000 Х4 1.124589 0.877192 1.282032 0.200 X5 0.3237421 0.060709 5.332661 0.000 constant 8.86016 1.766116 5.016749 0.000 What is the R2 for this sample? What information does the R² provide?

Is the y- intercept meaningful or meaningless?

What is a numerical prediction from the regression line equation shown in the photo?

5) Can i get help with this question please

A researcher’s results are shown below using Femlab (labor force participation rate among females) to try to predict Cancer (death rate per 100,000 population due to cancer) in the 50 U.S. states.   Regression Statistics Multiple R 0.313422848   R Square 0.098233882   Adjusted R Square 0.079447088   Standard Error 32.07003698   Observations 50       Variable Coefficients Standard Error t Stat Intercept   343.619889     61.0823514     5.62552   Femlab   –2.2833659     0.99855319     –2.28667     Which statement is valid regarding the relationship between Femlab and Cancer? Multiple Choice   At the .05 level of significance, there isn’t enough evidence to say the two variables are related.   This model explains about 10 percent of the variation in state cancer rates.   If your sister starts working, the cancer rate in your state will decline.   A rise in female labor participation rate will cause the cancer rate to decrease within a state.

Q1 Data displayed in this graph were standardized. True or false? Q2 Based on this graph, one can accurately estimate shoe size heritability to be 0.8676. True or false?

A student collected concentration versus absorbance data for a series of standards and produced a standard curve. Which value of 2 would reflect the linear regression curve the student produced? Absorbance 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 ² = 0.10 ² = 0.76 ² = 0.98 2 = 0.45 1 Absorbance vs. Concentration 2 3 Concentration (ppm) 4 5 6

Q2 Part b

Multiple regression analysis was used to study the relationship between a dependent variable, y, and four independent variables; x1, x2, x3, and x4. The following is a partial result of the regression analysis involving 31 observations.       Coefficients Standard Error     Intercept 18.00 6.00     x1 12.00 8.00     x2 24.00 48.00     x3 -36.00 36.00     x4 16.00 2.00     ANOVA   df SS MS F Regression     125   Error         Total   760       Compute the multiple coefficient of determination. Perform a t test and determine whether or not β1 is significantly different from zero (α = .05). Perform a t test and determine whether or not β4 is significantly different from zero (α = .05). At α = .05, perform an F test and determine whether or not the regression model is significant.

A) Which point from the data has the largest residual? B) Explain what the residual means in context. Is this point an outlier? An influential point? The residual means that when the swim time is______, the observed heart rate is about  _____  beats less than the predicted rate. When this point is removed, it has an effect on the regression line, so it is influential. The point is not an outlier, because the residual is less than twice the standard deviation.

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There is a dataset of size n = 51 and is for the 50 states and the District of Columbia in the United States. The dependent variable is year 2002 birth rate per 1000 females 18 to 19 years old and independent variable is the violent crime rate (per 1000 population). A simple linear regression model is run with the results given below. What is the Pearson correlation coefficient between x and y variables? The R squared of the model? What kind of relationship there is?

▼ Set background Clear frame 6.2 2) The program used to create this scatterplot found the line-of-best-fit and reported the r-squared value as r^2 = 0.805 for the relationship between arm-span and height for several individuals. What is the correlation coefficient? Is it positive or negative? Explain how you know. 1850- 1800- 1750- 1700- 00 1650- 1600- 1550- arm-span (cm) height (cm) 160.0 R Sq Linear-0805 : =S Open c 2 IN

sample of trucks and their "static weight" and "weight in motion" weights are in thousands of pounds. What percent of the variability in static weight can be explained by the linear model rounded to the nearest 10th?

3. You are working on a regression when accidentally you dump coffee all over yourregression output. Using the remaining values, find the missing values in each blank numbered 1-9 SUMMARY OUTPUT Regression Statistics Multiple R 0.860644193 R Square # 4 Adjusted R Square 0.72282625 Standard Error #5 Observations 32 ANOVA df MS F Significance F Regression 2 834.0726419 417.0363209 41.4216016 3.16178E-09 Residual #2 #1 # 3 Total 31 1126.047188 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 36.90833048 2.190798638 16.84697527 1.62066E-16 32.42764417 41.38901679 cyl -2.264693597 0.575889243 #7 0.000480375 -3.442519346 -1.086867847 hp #6 0.01500073 -1.274717745 0.212528465 #8 #9 Screenshot

Dep. Variable: Model: Method: Date: Time: No. Observations: Df Residuals: Df Model: Covariance Type: Intercept educ IQ KWW exper black feduc meduc Least Squares Wed, 23 Nov 2022 08:57:40 coef -503.0058 40.5455 3.1654 7.2094 16.2338 -59.2987 10.8278 5.0471 std err nonrobust 138.947 8.660 1.199 wage OLS 2.120 3.740 52.143 5.353 6.106 722 714 7 R-squared: Adj. R-squared: Prob (F-statistic): F-statistic: Log-Likelihood: AIC: BIC: -3.620 4.682 2.640 3.400 4.340 -1.137 2.023 0.827 P> [t] 0.000 -775.800 0.000 0.008 0.001 0.000 0.256 0.043 0.409 0.189 0.181 23.83 23.543 0.812 3.047 8.891 -161.671 0.318 -6.941 3.310-29 -5288.5 (0.025 0.9751 1.0590+04 1.063e+04 -230.212 57.548 5.519 11.372 23.577 43.074 21.338 17.035 Above is a regression of men's monthly earnings in dollars as a function of their years of education, their IQ score in "points", their Knowledge of the World of Work (KWW) score in points (a measure of their knowledge of how occupations work), their years of work experience…

The service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year.  The analyst ran a simple linear regression shown below. Table 7: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .854a .730 .695 6.6235 a. Predictors: (Constant), Hourly Wage   Table 8: ANOVA ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1918.458 1 1918.458 129.783 .000a Residual 709.567 48 14.782     Total 2628.025 49       a. Predictors: (Constant), Hourly Wage b. Dependent Variable: Number of Complaints   Table 9: Coefficients Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 20.2 4.357   4.636 .000 Hourly Wage -1.20 .088 -.946 -13.636 .000 a. Dependent Variable: Number of…

The following regression has been estimated: Dependent Variable: disability 1 Independent Variables: (1) Constant, (2) Person's age (ecage26), (3) male1 "male1" is equal to 1 for males and 0 for females; disability1 is equal to 1 for disabled and 0 for not disabled regress disabilityl ecage26 malel Source | Total | Model | 1137.25995 2 Residual | 9104.04738 47702 disabilityl I ecage26 | malel SS cons df 10241.3073 47704 Coef. Std. Err. .0084218 -.0084567 -.0891858 MS 568.629974 .19085253 .214684457 .0001093 .0040058 .0060068 t 77.04 -2.11 -14.85 47705 Number of obs = F( 2, 47702) = 2979.42 Prob > F R-squared Adj R-squared = Root MSE P>|t| 0.000 0.035 0.000 [95% CI] = = 0.0000 0.1110 0.1110 .43687 .0082076 -.0163082 -.1009592 .0086361 -.0006052 -.0774125 a) Report the estimated values (the actual numbers) of the coefficient estimates (including the constant) and explain what they measure. b) What are some economic reasons why the estimated coefficients might take on the signs (and…

Brain size Does your IQ depend on the size of yourbrain? A group of female college students took a test thatmeasured their verbal IQs and also underwent an MRI scan to measure the size of their brains (in 1000s of pix-els). The scatterplot and regression analysis are shown, and the assumptions for inference were satisfied.Dependent variable is: IQ_VerbalR-squared = 6.5% s = 21.5291 df = 18Variable Coefficient SE(Coeff)Intercept 24.1835 76.38Size 0.098842 0.0884a) Test an appropriate hypothesis about the associationbetween brain size and IQ.b) State your conclusion about the strength of thisassociation.

What percentage of the variation in  can be explained by the corresponding variations in  and  taken together?