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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 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 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

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

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

The regression equation is Health Index= y + a Age + ß Blood sugar + 8 Blood Pressure SE 20986 339.28 Age Blood sugar 209.2 Blood pressure 207.2 S = 962.233 R-Sq = 86.6% R-Sq (adj) = 76.5% Coef Constant Analysis of Variance Source DF Regression 3 Residual Error. 4 Total 7 SS Coef 2912 71.95 179.3 225.4 23863180 3703570 27566750 T 7.21 4.72 * 0.92 MS 7954393 925892 F *** P 0.002 0.009 0.308 ** P 0.032 a) What is dependent and independent variables? b) Fully write out the regression equation. c) Fill in the missing values **, ****, and *****. d) Hence test whether & is significant. Give reasons for your answer. e) Perform the F Test making sure to state the null and alternative hypothesis.

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.

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

To investigate the relationship between the milage and sales price for a popular car model the pictured scatterplot was used. a) Based on the excel output that's pictured, what is the estimated regression equation that could be used to predict the price given the miles? b) Does the model fit the data? (ie whether the regression relationship is statistically significant) Did the estimated regression equation provide a good fit? (ie use the coefficient of determination to explain variability independent variable) c) Suppose you are considering purchasing a car of this model with 60000 miles. Using the estimated regression equation, predict the price.

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.

What variables do Teczar find have the most significant controlled associations with women in national parliaments?

/was/ui/v2/assessment-player/index.html?launchid=831489a9-d255-4984-8d47-074c80bc17d6#/question Problems Question 2 of 8 Two variables are defined, a regression equation is given, and one data point is given. Weight Training = Weight = = maximum weight capable of bench pressing (pounds) number of hours spent lifting weights a week The data point is an individual who trains 5 hours a week and can bench 150 pounds. 99 + 11.7(Training) (a) Find the predicted value for the data point and compute the residual. Enter the exact answers. Residual = i II Predicted value = eTextbook and Media 3' " lbs lbs C D 6 O W

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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?

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?

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…