STA301 Final Term Current Solved Subjective 2022 by Prince Alvi

1. 2 q's means or variance k thy.

Variance = Variance of X = 2 = E[(X - )2] = (x - )2 P(x) MEAN AND VARIANCE OF THE POPULATION DISTRIBUTION Mean =

Variance =

2) Sampling distribution =

3) Normal distribution =

4. Ak integral of function Ka tha. Jo prove krna tha.

5) one tailed

A test, for which the entire rejection region lies in only one of the two tails – either in the right tail or in the left tail – of the sampling distribution of the test-statistic, is called a one-tailed test or one-sided test. H0 : p < 0.50 --- HA : p > 0.50 --- (one-tailed test)

6) hyper geometric =

10) chi- square distribution =

11. Significance level = We set the significance level at  = 0.05.
12. Variance E(Y) = Var(Y) = E(Y2) – [E(Y)]2

13) critical region or level of signifinacnac ko compare krna tha

The level of significance acts as a basis for determining the CRITICAL REGION of the test. For example, if we are testing H0: μ = 45 against H1: μ ≠45, our test statistic is the standard normal variable Z, and the level of significance is 5%, then the critical values are Z = +1.96 Corresponding to a level of significance of 5%, we have:

14) Expected value find krni the E(X),E(Y),E(XY) or Cov(X,Y) find krna tha

15) CORRELATION CO-EFFICIENT =

16) t-distribution find krni thi

17. Hypothesis null or alternative wala qustion tha ..

A null hypothesis should always be precise such as ‘the given coin is unbiased’ or ‘a drug is ineffective in curing a particular disease’ or ‘there is no difference between the two teaching methods’.

An alternative hypothesis is any other hypothesis which we are willing to accept when the null hypothesis H0 is rejected. It is customarily denoted by H1 or HA. A null hypothesis H0 is thus tested against an alternative hypothesis H1. For example, if our null hypothesis is H0 :  = 62, then our alternative hypothesis may be H1 :   62 or H1 :  < 62

18) Poisson distribution 3 properties

PROPERTIES OF THE POISSON DISTRIBUTION Some of the main properties of the Poisson distribution are given below:

•  If the random variable X has a Poisson distribution with parameter , then its mean and variance are given by E(X) =  and Var(X) = .
•  (In other words, we can say that the mean of the Poisson distribution is equal to its variance.)
•  The shape of the Poisson distribution is positively skewed. The distribution tends to be symmetrical as  becomes larger and larger.

19. 1 binomial k through X k value find krni thi. P and q k values given thin.