Question

1) (10 pts total) Given f is some probability density function, fill in the blank with the common name, value or function name for each of the following equations. Note that fo is not necessarily the same from one equation to the next. In some cases there may be more than one correct answer, e.g., "E(X)" or "ux", in which case you only have to provide one of them. X + z = to ma zema ___= (49) where, e = |x-ul Erroneously accepting a null hypothesis when it should be rejected = Erroneously rejecting a valid null hypothesis when it happens to be true = = The probability of obtaining a result equal to or "more extreme" than what was actually observed, assuming Ho is true = bo + bix 2=1(yi – bo – bıx)2 Σvi-byΣαι (xi-x)(yi-y) (xi-x)2

Given f(.) is some probability density function, fill in the blank with

Answer 1

1. This is the confidence interval based on the z score where population standard deviation is known. Thus this is the confidence interval for population mean of a Normal or an approximate Normal distribution where population SD is known, at level of significance c.

2. This is the confidence interval based on the t score where population standard deviation is unknown. Thus this is the confidence interval for population mean of a t distribution where population SD is estimated, at level of significance c.

3. This is the confidence interval based on the z score where population standard deviation of both the populations are known. Thus this is the confidence interval for the difference of population mean of 2 Normal or an approximate Normal distributions where population SDs are known, at level of significance c.

4. This is known as Standardised error.

5. Type II error, by definition

6. Type I error, by definition

7. p-value, by definition.

8. linearly regressed variable, by definition.

9. Sum of squares of errors in a linear regression model or one-way ANOVA model, by structure.

10. This is, by formula, the estimate of the intercept in the linear regression model.

11. This term is, by formula, byx i.e. the regression coefficient

$b_{yx}=r\frac{s_y}{s_x}$

where r is the correlation coefficient between x and y, sy and sx are the respective standard deviations of y and x.

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