How forecasting is quite different from the estimation of causal effects.?
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How forecasting is quite different from the estimation of causal effects.?
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- Read through this scenario and look at the data that was collected. State the null and all possible research hypotheses. Review the results below (I used SPSS) and answer the questions that follow. Scenario: A researcher wants to see if gender and / or income affects the total amount of help given to a stranger who is sitting on the side of a busy road with a sign asking for help. The independent variables are gender, income, and the interaction of gender and income. The dependent variable is total help. He wants to know if one or both factors – or the interaction of the two - affects the total amount of help offered. Because he is analyzing two independent variables (gender and income), he used a factorial ANOVA. His results show the main effect of each of the independent variables on the dependent variable (total help) and the interaction effect. The researcher is using a 95% confidence interval which means that he wants to be at least 95% sure that his independent variables…You are concerned that nausea may be a side effect of Tamiflu, but you cannot just give Tamiflu to patients with the flu and say that nausea is a side effect if people become nauseous. However, past research indicates that about 30% of people who get the flu experience nausea, and you believe that the percentage of those who experience nausea while having the flu and taking Tamiflu will be greater than 30%, which would indicate that nausea is a side effect of Tamiflu.If you are going to test this claim at the 0.05 significance level, what would be your null and alternative hypotheses? H0: ? p = p ≠ p < p > p ≤ p ≥ μ = μ ≠ μ < μ > μ ≤ μ ≥ H1: ? p = p ≠ p < p > p ≤ p ≥ μ = μ ≠ μ < μ > μ ≤ μ ≥ What type of hypothesis test should you conduct (left-, right-, or two-tailed)? left-tailed right-tailed two-tailedWhat is the shape of this data analysis?
- You are concerned that nausea may be a side effect of Tamiflu, but you cannot just give Tamiflu to patients with the flu and say that nausea is a side effect if people become nauseous. However, past research indicates that about 30% of people who get the flu experience nausea, and you believe that the percentage of those who experience nausea while having the flu and taking Tamiflu will be greater than 30%, which would indicate that nausea is a side effect of Tamiflu.a) If you going to test this claim at the 0.05 significance level, what would be your null and alternative hypotheses?H0H0: H1H1: b) What type of hypothesis test should you conduct (left-, right-, or two-tailed)? left-tailed right-tailed two-tailedDoes a high value of r2 allow us to conclude that two variables are causally related? Explain. - A high value of r2 can only allow us to conclude that two variables are causally related in linear relationships, but not in nonlinear relationships. - Yes. Regression or correlation analysis always allows us to conclude that two variables are causally related. - A high value of r2 can only allow us to conclude that two variables are causally related in nonlinear relationships, but not in linear relationships. - No. Regression or correlation analysis can never allow us to conclude that two variables are causally related. - Yes. Since r2 is the percentage of the total sum of squares that can be explained by using the estimated regression equation, a high value of r2 allows us to conclude that two variables are causally related.Give a plausible example of a three-variable research problem in which partial correlation would be a useful analysis. Define X1, X2, and Y. Make sure that you indicate which of your three variables is the "controlled for" variable ( X2). What results might you expect to obtain for this partial correlation, and how would you interpret your results (e.g., spurious correlation, mediation, moderation, and so on)?
- Discuss the difference between a Type I error and a Type II error. Is it easier to commit one type of error than it is to commit another? Is one type of error more detrimental than another? Why or why not?The Dependent variable is the Effectiveness of a new therapeutic technique or Recall on intrusive thougnts? Why?Olivia sees that in her data there is a relationship between regular exercise and a range of health outcomes, with those doing exercise having better outcomes. Olivia can claim which of the following statements A. there is evidence of a correlation between regular exercise and health outcomes in her group of UTS academics B. there is evidence of a causal relationship between regular exercise and health outcomes in the population of UTS academics C. there is evidence of a causal relationship between regular exercise and health outcomes in her group of UTS academics D. there is evidence of a correlation between regular exercise health outcomes in the population of UTS academics O She can claim only statement A O She can claim only statement C She can claim all statements O She can claim statements A and C O She can claim statements A and D She can claim statements B and C
- A scientist, who wants to study the effects of precipitation on rice crops, sets up an experiment with 4 groups of rice crops. Rice crop harvest vs. precipitation level Group B Group A 0 150 Group C Precipitation (cm/annually) Harvest (pounds/acre) 0 60 Which statement best describes the relationship between the dependent and independent variables in this study? 20 O The amount of precipitation is dependent on the amount of rice harvest. O The amount of rice harvest is positively correlated with the increase in precipitation. The amount of rice harvest is not affected by the amount of precipitation. The amount of rice harvest is negatively correlated with the increase in precipitation. 200 Group D 40 300Based on a graph below which of the following statement describes the best types of associations between Factors A, C, and D and the Disease: (choose one best answer) Factor Factor C Disease Factor A O Factor A is sufficient but not necessary, while Factors C and D and neither sufficient not necessary. O Factors A, C and D are neither sufficient nor necessary. O Factor A is necessary but not sufficient, while Factors C and D are necessary but not sufficient. O Factor A is sufficient but not necessary, while Factors C and D are neither sufficient not necessary.Does a high value of r2 allow us to conclude that two variables are causally related? Explain. - A high value of r2 can only allow us to conclude that two variables are causally related in linear relationships, but not in nonlinear relationships. - Yes. Regression or correlation analysis always allows us to conclude that two variables are causally related. - A high value of r2 can only allow us to conclude that two variables are causally related in nonlinear relationships, but not in linear relationships. - No. Regression or correlation analysis can never allow us to conclude that two variables are causally related. - Yes. Since r2 is the percentage of the total sum of squares that can be explained by using the estimated regression equation, a high value of r2 allows us to conclude that two variables are causally related.
