Download
1 / 28
280 likes | 420 Vues
Chapter 2. Hypothesis Testing Test for one and two means Test for one and two proportions. Hypothesis Tests: An Introduction. We test a certain given theory / belief about population parameter.
E N D
Chapter 2 Hypothesis Testing Test for one and two means Test for one and two proportions
Hypothesis Tests: An Introduction • We test a certain given theory / belief about population parameter. • We may want to find out, using some sample information, whether or not a given claim / statement about population is true. Hypothesis and Test Procedures A statistical test ofhypothesis consist of : 1. The Null hypothesis, 2. The Alternative hypothesis, 3. The test statistic and its p-value 4. The rejection region 5. The conclusion
Definitions Hypothesis Testing: It is a process of using sample data and statistical procedures to decide whether to reject or not to reject the hypothesis (statement) about a population parameter value (or about its distribution characteristics). Null Hypothesis, : A null hypothesis is a claim (or statement) about a population parameter that is assumed to be true. (the null hypothesis is either rejected or fails to be rejected.) Alternative Hypothesis, : An alternative hypothesis is a claim about a population parameter that will be true if the null hypothesis is false. The rejection of the null hypothesis will imply the acceptance of this alternative hypothesis.
Test Statistic: It is a function of the sample data on which the decision is to be based. Critical/ Rejection region: It is a set of values of the test statistics for which the null hypothesis will be rejected. Critical point: It is the first (or boundary) value in the critical region. P-value: The probability calculated using the test statistic. The smaller the p-value is, the more contradictory is the data to .
Procedure for hypothesis testing 1. Define the question to be tested and formulate a hypothesis for a stating the problem. • Choose the appropriate test statistic and calculate the sample statistic value. The choice of test statistics is dependent upon the probability distribution of the random variable involved in the hypothesis. • Establish the test criterion by determining the critical value and critical region. 4. Draw conclusions, whether to accept or to reject the null hypothesis.
Example: The average monthly earnings for women in managerial and professional positions is RM 2400. Do men in the same positions have average monthly earnings that are higher than those for women? A random sample of n = 40 men in managerial and professional positions showed x = RM3600 and s = RM 400. Test the appropriate hypothesis using α = 0.01. Solution: The hypothesis to be tested are: (claim) We use normal distribution n > 30 Test statistic:
Rejection region: Since 18.97 > 2.33, falls in the rejection region, we reject and conclude that average monthly earnings for men in managerial and professional positions are significantly higher than those for women.
Example: Aisyah makes “kerepek ubi” and sell them in packets of 100g each. 12 randomly selected packets of “kerepek ubi” are taken and their weights in g are recorded as follows: Perform the required hypothesis test at 5% significance level to check whether the mean weight per packet if “kerepek ubi” is not equal to 100g. Solution: The hypothesis to be tested are: We use t distribution, Test Statistic:
Two-tailed test Rejection Region: From t-table ( ): Since – 2.737 < -2.201, falls in the rejection region, we reject and conclude that weight per packet of “kerepek ubi” is not equal to 100g.
Exercise: A teacher claims that the student in Class A put in more hours studying compared to other students. The mean numbers of hours spent studying per week is 25hours with a standard deviation of 3 hours per week. A sample of 27 Class A students was selected at random and the mean number of hours spent studying per week was found to be 26hours. Can the teacher’s claim be accepted at 5% significance level?
Hypothesis Testing for the Differences between Two Population Mean, Test hypothesis:
Example: The mean lifetime of 30 bateries produced by company A is 50 hours and the mean lifetime of 35 bulbs produced by company B is 48 hours. If the standard deviation of all bulbs produced by company A is 3 hour and the standard deviation of all bulbs produced by company B is 3.5 hours, test at 1 % significance level that the mean lifetime of bulbs produced by Company A is better than that of company B. Solution: We reject . The mean lifetime of bulbs produced by company A is better than that of company B at 1% significance level.
Example: A mathematic placement test was given to two classes of 45 and 55 student respectively . In the first class the mean grade was 75 with a standard deviation of 8, while in the second class the mean grade was 80 with a standard deviation of 7. Is there a significant difference between the Performances of the two classes at 5% level of significance? Assume the population variances are equal. Solution:
Since , so we reject So there is a significant difference between the performance of the two classes at 5% level of significance.
Exercise: A sample of 60 maids from country A earn an average of RM300 per week with a standard deviation of RM16, while a sample of 60 maids from country B earn an average of RM250 per week with a standard deviation of RM18. Test at 5% significance level that country A maids average earning exceed country B maids average earning more than RM40 per week.
Example: A manufacturer of a detergent claimed that his detergent is at least 95% effective is removing though stains. In a sample of 300 people who had used the detergent,n 279 people claimed that they were satisfied with the result. Determine whether the manufacturer’s claim is true at 1% significance level. Solution: Since , we accept The manufacturer’s claim is true at 1% significance level.
Exercise: When working properly, a machine that is used to make chips for calculators produce 4% defective chips. Whenever the machine produces more than 4% defective chips it needs an adjustment. To check if the machine is working properly, the quality control department at the company often takes sample of chips and inspects them to determine if they are good or defective. One such random sample of 200 chips taken recently from the production line contained 14 defective chips. Test at the 5% significance level whether or not the machine needs an adjustment.
Example: In a process to reduce the number of death due the dengue fever, two district, district A and district B each consists of 150 people who have developed symptoms of the fever were taken as samples. The people in district A is given a new medication in addition to the usual ones but the people in district B is given only the usual medication. It was found that, from district A and from district B, 120 and 90 people respectively recover from the fever. Test the hypothesis that the new medication helps to cure the fever using a level of significance of 5%. Solution:
Since so we reject The new medication helps to cure the fever at 5% significance level.
Exercise: A researcher wanted to estimate the difference between the percentages of two toothpaste users who will never switch to other toothpaste. In a sample of 500 users of toothpaste A taken by the researcher, 100 said that they will never switched to another toothpaste. In another sample of 400 users of toothpaste B taken by the same researcher, 68 said that they will never switched to other toothpaste. At the significance level 1%, can we conclude that the proportion of users of toothpaste A who will never switch to other toothpaste is higher than the proportion of users of toothpaste B who will never switch to other toothpaste?
