2)and a new HPLC instrument. Compare the 2 data sets using graphed regression analysis in Excel tosee if the methods can be determined as equal. Discuss your conclusion. You should provide ascreengrab of your graph(s).The level of phthalates in 20 water samples was determined by an old HPLC instrumentOld HPLCNew HPLC110.110.5211.311.0312.411.449.89.358.68.39.28.9710.210.3810.610.38.99.31010.810.91111.511.01212.412.11310.210.8149.89.9158.47.9168.48.01710.410.9189.810.5199.310.62011.211.1

S.L. No. Old HPLC New HDLC 1 10.1 10.5 2 11.3 11 3 12.4 11.4 4 9.8 9.3 5 8.6 8.3 6 9.2…

Answered: 2) and a new HPLC instrument. Compare…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Please see attached image. In analyzing the effects of an after-school reading program, you run a regression analysis with program participation as the independent variable (0 = control group; 1 = intervention group) and scores on a reading comprehension exam after the program as the dependent variable. Is the effect of the after-school reading program statistically significant? How can you tell, and what does this mean?
Please help me with question d, e, f, g, h, and I. Thank you.
Fit these three regression models and then discuss the similarities and differences between them, particularly as relates to slope estimates (use CI’s) and R2. Also address why this is a “special case” and we wouldn’t necessarily expect to see these model characteristics for a typical dataset. a) Additive model including both predictors (output attached) b) Model including only Moisture (output attached) c) Model including only Sweetness BrandLiking = 68.62 + 4.38 Sweetness Term 95% CI P-ValueConstant (50.16, 87.09) 0.000Sweetness (-1.46, 10.21) 0.130 S R-sq R-sq(adj)10.8915 15.57% 9.54%
The Conde Nast Traveler Gold List for 2012 provided rating for the top 20 small cruise ships. The data from annual Readers’ Choice Survey are the overall scores(Y) each ship received based on several criteria, including Itineraries/Schedule (X1), Shore Excursions(X2), and Food/Dinning(X3). The estimated regression equation to predict the overall scores is Y= 35.6184+0.1105 X1+0.2445 X2+0.2474 X3. Part of the regression results is shown below. Coefficients Standard Error Intercept 35.6184 13.2308 Itineraries/Schedule(X1) 0.1105 0.1297 Shore Excursions(X2) 0.2445 0.0434 Food/Dinning(X3) 0.2474 0.0621 Use the T test to determine whether or not the coefficient of X1 is significant. Use Level of significance=.05? Be sure to state null and alternative hypotheses.…
Last ride Consider the roller coasters described inExercise 26 again. The regression analysis gives themodel Duration = 64.232 + 0.180 Drop.a) Explain what the slope of the line says about howlong a roller coaster ride may last and the height of thecoaster.b) A new roller coaster advertises an initial drop of200 feet. How long would you predict the rides last?c) Another coaster with a 150-foot initial drop advertisesa 2-minute ride. Is this longer or shorter than you’dexpect? By how much? What’s that called?
A researcher plans to study the causal effect of police on crime using data from a random sample of U.K. counties. He plans to regress the county’s crime rate on the (per capita) size of the county’s police force. Explain why this regression is likely to suffer from omitted variable bias. Which variable would you add to the regression to controlfor important omitted variable? Determine whether the regression will likely over or underestimate the effect of police on the crime rate?
The following table shows the starting salary and profile of a sample of 10 2 p employees in a certain call center agency. Run a multiple regression analysis with starting salary as the dependent variable (pesos) and GPA, years of experience and civil service ratings as the independent variables. Use .05 level of significance.What is the computed R square of the resulting multiple linear regression and its interpretation? * Civil Years of Starting salary GPA service experience ratings 79.5 15000 80.1 15000 81.2 78.0 15500 81.3 79.0 16000 82.4 80.0 16200 83.4 85.0 17500 87.9 89.9 89.1 18000 90.3 16,300 84.2 17000 87.0 17900 88.1 84.1 89.0 89.2 R squared = 0.8053; This means that 80.53% of the total variation in the starting salary can be explained by its linear relationship with GPA, years of experience and civil service ratings. R squared = 0.9651; This means that 96.51% of the total variation in the starting salary can be explained by its linear relationship with GPA, years of…
There exists a moderate relationship between the variables x and y.if we draw samples of increasing sample size,what can we expect from the sample r value,the regression slope and the p value of the regression or correlation be affected by increasing sample size
The fish in my pond have mean length 10 inches and mean weight 4 pounds. The correlation coefficient of length and weight is .9. If the length of a particular randomly selected fish is reported to be 10 inches, then what should we predict for the weight of that fish using simple linear regression? 4 10 2.5 NOT ENOUGH INFORMATION GIVEN NONE OF THE OTHERS
What is the slope of the regression line (slope and intercept) Please put the answer in bold.
Could you help me with these 2 please
Provide an example of a regression that arguably would have a high value of R² but would produce biased and inconsistent estimators of a causal effect. Explain why the R² is likely to be high. Explain why the OLS estimators would be biased and inconsistent.

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