1. A statistical hypothesis is either a statement about the value of a population parameter (e.g., mean, median, mode, variance, standard deviation, proportion, total), or a statement about the kind of probability distribution that a certain variable obeys.A statistical hypothesis test is a method of making decisions using data, whether from a controlled experiment or an observational study (not controlled). In statistics, a result is called statistically significant if it has been predicted as unlikely to have occurred by chance alone, according to a pre-determined threshold probability, the significance level. 2. What is a null hypothesis?
It all depends on how sensitive the mean is, for example when it is very sensitive in the extreme values and the distribution is not symmetrical, and the mean will be away from the center and more near the extreme values. In statistics normality is important so the underlying population is normally distributed. (Doane & Seward, 2007) * What effect does sample size, n, have on the estimate of the mean? Is it possible to normalize the data when the population shape has a known skew? How would you demonstrate the central limit theorem to your classmates?
According to both rules, the sample size is small. (d) Why might collinearity account for the lack of significance of some predictors? Collinearity refers to a strong correlation between two variables. This strong correlation makes it difficult or impossible to estimate their individual regression coefficients reliably (Statistics.com, 2010). In this case rebounds and points are highly
Answer: False Difficulty: Medium 5. The sampling distribution of a sample statistic is the probability distribution of the population of all possible values of the sample statistic. Answer: True Difficulty: Easy 6. A sample statistic is an unbiased point estimate of a population parameter if the mean of the populations of all possible values of the sample statistic equals the population parameter. Answer: True Difficulty: Medium 7.
Explain. Suppose that you perform a significance test regarding a population mean and that the evidence does not warrant rejection of the null hypothesis. When formulating the conclusion to the test, why is the phrase fail to reject the null hypothesis more accurate than the phrase accept the null hypothesis? Why can the null hypothesis not be proved? Explain.
The unexplained sum of squares measures the variability of the dependent variable about a. the mean of the Y values. b. the regression line c. the mean of the X values d. the Y-intercept 7. Which of the following test statistic is used to check the normality assumption of the error in a regression model? a. t statistic. b. F statistic c. histogram of residuals.
(i) The data member cost can be set to a new value, by a user of this class (ii) cout<<”The rose has: “ << Rose.getPetals() << “ petals.”; is a valid statement (iii) There is a destructor in the class (Points : 2) i. ii Only i and iii None are true All are true | 8. (TCO 2) Can two methods each define a local variable with the same name? (Points : 2) Yes, as long as the variable is used in the same way. No, this is not possible because the compiler would not know which variable to use. Yes, but only if the methods have the same name.
What are you going to do with the outlier? Please provide a rationale for your decision. (0 marks as this question is part of data screening for the writing of the results in Task 10) Participant two’s score was changed to 256, which was one unit higher than the next most extreme score identified. Perform data screening. Was there a normal distribution?
Causation and Correlation Causation and correlation both find means of determining factors that bring about some form of outcome. Causation views the presence of one or more factors in combination that cause a result/outcome; whereas, correlation is the relationship among variables that produce the results or outcome. When factors are not present in causation there is no results while in correlation there may be strong, weak, positive and negative factors that affect the types of outcomes that are resulted. Information that supports on point can either positively affect the other point of a correlation causing a positive result or a point may be positive and negatively affect the other. An example of a correlation statement would be, “the rich keep getting richer and the poor keep getting poorer.” In this correlation statement the money would be the lurking variable because individuals that are well employed may earn more money, enabling these people to easily save money; while individuals that do not have employment or lower paying employment would have less money to save.
For each, give (a) the Z-score cutoff (or cutoffs) on the comparison distribution at which the null theory should be turned down, (b) the Z score on the comparison distribution for the sample score, and (c) your conclusion. Assume that all populations are normally distributed. Study A: z –score = (7 – 5)/1 = 2 At 0.05 significance level, the z-score is + 1.64 z-score cut-off value = +1.64 As 2 > 1.64 The null theory is turned down. Study B: z –score = (7 – 5)/1 = 2 At 0.05 significance level for two-tailed test, the z-score is ±1.96. z-score cut-off value = ±1.96 Because 2 > +1.96 The null theory is turned down.