Some true and false questions about statistics?
Get as many as you can please, thank you :)! 1.For normally distributed populations, if two samples are independent and the variances are known, the z-test is used to compare two populations parameters. 2. When hypothesizing a difference of 0 if the confidence interval does not contain 0, the null hypothesis is rejected. 3. When Comparing two variances or standard deviations, a t-test is used. 4. For a case of testing the difference between two large sample means, in the figure below, if the z-test value is 1.43, the null hypothesis should not be rejected. 5. When conducting a two tailed z test with Q= 0.01, the test value was 2.07. The decision would be do not reject the null hypothesis 6. The t-distribution must be used when the sample size is greater than 30 and the variable is normally or approximately normally distributed 7. An assumption of the chi-square test for a single variance is that the observations must be independent of each other. 8. A regression line was calculated as y= 9.7 - 3.2x. The slope is: -3.2 9. Samples are independent when they are not related 10.A positive relationship exists when both variables increase or decrease at the same time.
True or false statistics question?
A one-sample z-test for a population mean is to be performed. Let z0 denote the observed value of the test statistic, z. Assume that a two-tailed test is being performed. True or false: If z0 is negative, the P-value is twice the area under the standard normal curve to the right of z0. A) True B) False
Stats true or false question help?
Suppose alpha = 5%, test statistic is z = 1.70, and test is right tail. z critical = 1.645, so we reject Ho. If instead test is two tail, alpha/2 = 2.5%, z critical = 1.96, and we do not reject H0. Answer is false.
A two tailed test with n=9 and test stat t= -1.577?
p = 0.1534 t-statistic = -1.577, degrees of freedom = 8 Two tailed test: P(t < -1.577) + P(t > 1.577) = 0.0767 + 0.0767 = 0.1534
Stats help please! Two-Tailed Hypothesis Test?
I wish to help to some extent. a) Ho: There is NO significant difference between the average depression for elderly people and average depression in the general population. Mu = 40 b) Ha: There is significant difference between the average depression for elderly people and average depression in the general population. c) Calculate Xbar and Standard deviation (S) for the sample data. then Calculate t-value = (Xbar - Mu) *sqrt n / S d) Degrees of freedom = n - 1 = 9 - 1 = 8 Critical value of t for 8 d.f at alpha = 0.05 is 2.306 e) Compare the calculated value of t with the critical value and Reject Ho, if the calculated t value > critical value of t Accept Ho, if the calculated t value < critical value of t Draw the inference accordingly.
How can one interpret a negative t-value in a student test (p-value is very low)?
When doing a t-test, the alternative hypothesis can either be one-sided (for example, [math]\mu < 10[/math] or [math]\mu>8[/math]) or two-sided (for example, [math]\mu \ne 0[/math]).If you are doing a two-sided test, then finding that the sample mean is larger or smaller than the hypothesized mean should be taken as evidence against the null hypothesis and in favor of the alternative. (Then the p-value is computed to quantify the strength of the evidence.) But what matters in the context of your question is that the sample mean can be smaller OR larger than the hypothesized mean and still be interpreted as evidence against the null hypothesis. If it is smaller than the hypothesized value, then the t-statistic will be negative. If it is larger, the t-statistic will be positive. But it really makes no difference which sign it has because both are signs are interpreted the same way - as evidence against the null hypothesis.However, if you are doing a one-sided test, this isn't true anymore. For example, suppose the alternative hypothesis is [math]\mu>0[/math]. In this case, if the sample mean is negative, that is NOT evidence against the null in favor of the alternative. (You wouldn't reject the null that the true mean is zero in favor of the alternative that it is positive if the data suggests that the mean is negative.) In the case of a one-sided alternative, the sign of the t-statistic matters A LOT. A negative sign implies that the sample mean is less than the hypothesized mean. This would be evidence against the null hypothesis IF (and only if) the alternative was that the true mean is LESS than the hypothesized value. A positive sign implies that the sample mean is larger than the hypothesized mean. This would be evidence against the null hypothesis IF (and only if) the alternative was that the true mean is GREATER than the hypothesized value.Depending on the software you are using, the p-value that is calculated may or may not take into account the alternative hypothesis. So be sure that you think about what your data tells you before you decide that the p-value really IS small. Because if the test is one-sided and the data points in the opposite direction of the alternative hypothesis, then the p-value CANNOT be small. It must be larger than 50% (even if your software tells you something else).