The researcher performs an analysis of variance (ANOVA) to test the hypothesis that the populations defined by the treatment combinations are equal.The results are presented in the following ANOVA table.SourceBetween treatmentsFactor AFactor BAXB interactionWithin treatmentsTotalANOVA TableSS1,607.187537.8120645.9380 3923.4400 35,127.4975 726,734.6875 79df71 37.8120 0.53215.3127 3.02307.8133 4.3271.2152MSFUse the significance level a = 0.05 to complete the following conclusions.The critical F value for factor A (years in military) is 3.97. Therefore, the main effect due to factor A isThe critical F value for factor B (program component) is 2.73. Therefore, the main effect due to factor B isThe critical F value for the interaction of factors A and B is 2.73. Therefore, the effect due to the interaction of factors A and B is Given the results of the preceding analysis, what does the researcher conclude?O Veterans who took different components of the program had different PTSD symptom scores. The severity of PTSD symptoms wasunrelated to length of service either by itself or in conjunction with the various components of the program.O Neither length of service nor program component was related to the severity of PTSD symptoms when looked at alone. However, therewas an interaction between the two factors such that severity differed by program component differently depending on length of service(and vice versa).O Veterans who took different components of the program had different PTSD symptom scores. By itself, length of service was notassociated with differences in severity; however, the various components of the program resulted in different degrees of PTSD symptomsdepending on length of service.O Length of service and program component were each related to the severity of PTSD symptoms. However, the effect of each factor wasindependent of the other.Measure the effect size for factor A (years in military), factor B (program component), and the interaction by computing (partial) eta squares.(Express the value of each eta square as a percent.)n² for factor A (years in military)=n² for factor B (program component)n² for the interaction =

The output of the two-way ANOVA is given. Source SS df MS F Critical value Between 1607.1875 7…

Answered: Use the significance level a = 0.05 to…

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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In addition to therapy, anti-depressant drugs are used in the treatment of depression. The data below are from a study comparing two anti-depressant drugs. A baseline measurement for symptomatic behavior for depression is taken. After treatment with medication, another measurement taken. The data given in the table is for improvement which is for the reduction in symptomatic behavior.
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Suppose we obtain data on prices of big-screen televisions and estimate the following model: In(Price) = 4.06 + 0.06 * Size +0.23 * Wide + 0.34 * Plasma + 0.21 * LCD+0.09 * Memory where the dependent variable has been transformed, Size is the screen size measured in inches, Wide is a dummy variable equal to one if the television is a widescreen, Plasma is a dummy variable equal to one if the television is a Plasma screen, LCD is equal to one if the television is an LCD screen, and Memory is a dummy variable equal to one if the television has any memory card slots. What is the estimated price of a 42" Widescreen Plasma television with 2 memory card slots? Select one: O a. 7.24 O b. 7.33 1525.38 O d. 1394.09 • e 1881.83 Clear my choice
Fiske Corporation manufactures a popular regional brand of kitchen utensils. The design and variety has been fairly constant over the last three years. The managers at Fiske are planning for some changes in the product line next year, but first they want to understand better the relation between activity and factory costs as experienced with the current products. Discussions with the plant supervisor suggest that overhead seems to vary with labor-hours, machine-hours, or both. The following data were collected from last three year's operations: Quarter Machine-Hours 18,850 4 5 6 7 8 9 10 11 12 18,590 17,480 19,240 21,280 19,630 19,240 18,850 18,460 20,670 17,550 18,460 Labor-Hours 15,605 15,484 16,727 15,990 17,508 17,376 15,297 14,373 16,001 17,002 14,285 17,651 Factory costs $ 3,395,671 3,425,836 3,617,844 3,573,940 3,812,984 3,778,012 3,532,426 3,369,802 3,513, 187 3,731,434 3,325,615 3,724,486 Required: Prepare a scatter graph based on the factory cost and labor-hour data. Note: 1.…
Students also often confuse the idea of Lurking Variable and Confounding Variable, but these are different but possibly related. This discussion is designed for students to help each other build a complete understanding, and clearly separate these as two distinct concepts. Discuss the similarities and differences between these, as well as what they imply for the conclusions of a research study.
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You are interested in understanding the impact of Student Teacher Ratios (STR) on Test Scores (i.e., STR is your variable of interest and Test Scores are your outcome variable). Let’s pretend you have 2 models and you must only pick 1. The models along with mock results are in the table below. The coefficients are presented with standard errors in parenthesis. Further assume that the STR coefficient in Model 1 is less biased than the STR coefficient in Model 2. But as you can see, the STR coefficient in Model 2 is more precisely estimated (it’s statistically significant) than the coefficient in Model 1. Which model (Model 1 or Model 2) do you prefer and why. X1 and X2 are different control variables. You do not need to know what they are in order to answer this question – you only need to know that they are different. Y = Test Scores Model 1 Model 2 STR -1.4 (0.9) -3.7** (0.9) X1 5.6** (2.2) X2 4.6** (2.1) Constant 680*** (45)…
I would like to know if I answered question number 1 of my homework correctly.
A police academy has just brought in a batch of new recruits. All recruits are given an aptitude test and a fitness test. Suppose a researcher wants to know if there is a significant difference in aptitude scores based on a recruits’ fitness level. Recruits are ranked on a scale of 1 – 3 for fitness (1 = lowest fitness category, 3 = highest fitness category). Using “ApScore” as your dependent variable and “FitGroup” as your independent variable, conduct a One-Way, Between Subjects, ANOVA, at α = 0.05, to see if there is a significant difference on the aptitude test between fitness groups. Identify the correct symbolic notation for the alternative hypothesis. A. H0: µ1 = µ2 B. HA: µ1 ≠ µ2 ≠ µ3 C. H0: µ1 = µ2 = µ3 D. HA: µ1 ≠ µ2
The marketing research department of a large company knows that the company's monthly sales are influenced by the way in which it spends money on marketing. For example, monthly expenditures such as the ones listed below are known to have an effect on y, the company's total monthly sales (in millions of dollars). x = money spent on television advertising (in 1000's of dollars) X2 = money spent on promotion (i.e., free samples) x3 = money spent on newspaper advertising (in 1000's of dollars) X = average discounts offered to retail outlets (in %) Using data from the previous 26 months, the company decides to collect data on 3 of the independent variables to use in a multiple regression model for estimating monthly sales. If the R´ for this model is 0.5, fill in the missing entries in the ANOVA table associated with this model. Do all calculations to at least three decimal places. Source of variation Degrees of freedom Sums of Mean F statistic squares squares Regression Error Total 4+
The relationship between United States unemployment and inflation rates during the Covid-19 pandemic. Background The covid-19 pandemic has led to higher unemployment rates in the United States than normal because many people were laid off jobs in response to the pandemic (Al-Fadly, 2020). It is important to analyze how such as sharp increase in unemployment affected inflation in relation to how unemployment have traditionally affected inflation. The overall relationship between unemployment and inflation has traditionally been an inverse one. It is argued that when unemployment rates are higher, the number of individuals who are looking for jobs usually becomes higher than the number of job positions that are available (Putnam & Azzarello, 2015). In such a situation, employers tend to pay lower wages because there is abundance of workers which naturally leads to a lower wage inflation. This study will examine the relationship between unemployment rates and inflation rate during…
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