Using the t-distribution, it is found that since the p-value of the test is of 0.0302, which is <u>less than the standard significance level of 0.05</u>, the data provides convincing evidence that the average food intake is different for the patients in the treatment group.
At the null hypothesis, it is <u>tested if the consumption is not different</u>, that is, if the subtraction of the means is 0, hence:
At the alternative hypothesis, it is <u>tested if the consumption is different</u>, that is, if the subtraction of the means is not 0, hence:
Two groups of 22 patients, hence, the standard errors are:
The distribution of the differences is has:
The test statistic is given by:
In which 
is the value tested at the null hypothesis.
Hence:
The p-value of the test is found using a <u>two-tailed test</u>, as we are testing if the mean is different of a value, with <u>t = 2.2438</u> and 22 + 22 - 2 = <u>42 df.</u>
- Using a t-distribution calculator, this p-value is of 0.0302.
Since the p-value of the test is of 0.0302, which is <u>less than the standard significance level of 0.05</u>, the data provides convincing evidence that the average food intake is different for the patients in the treatment group.
A similar problem is given at brainly.com/question/25600813
Answer:
20g
since mass should be in kg or gram if small then g
Answer:
0.72
Step-by-step explanation:
Adding (or subtracting) a constant to every data value adds (or subtracts) the same constant to measures of position such as center,percentiles, max or min.
Its shape and spread such as range, IQR, standard deviation remain unchanged.
When we multiply (or divide) all the data values by any constant, all measures of position (such as the mean, median, and percentiles) and measures of spread (such as the range, the IQR, and the standard deviation) are multiplied (or divided) by that same constant.
Part A:
The lowest score is a measure of location, so both addition and multiplying the lowest score of test B by 40 and adding 50 to the result will affect the lowest score of test A.
Thus, the lowest score of test A is given by 40(21) + 50 = 890
Therefore, the lowest score of test A is 890.
Part B:
The mean score is a measure of location, so both
addition and multiplying the mean score of test B by 40 and adding 50
to the result will affect the lowest score of test A.
Thus, the mean score of test A is given by 40(29) + 50 = 1,210
Therefore, the mean score of test A is 890.
Part C:
The standard deviation is a measure of spread, so multiplying the standard deviation of test B by 40 will affect the standard deviation but adding 50
to the result will not affect the standard deviation of test A.
Thus, the standard deviation of test A is given by 40(2) = 80
Therefore, the standard deviation of test A is 80.
Part D
The Q3 score is a measure of location, so both
addition and multiplying the Q3 score of test B by 40 and adding 50
to the result will affect the Q3 score of test A.
Thus, the Q3 score of test A is given by 40(28) + 50 = 1,170
Therefore, the Q3 score of test a is 1,170.
Part E:
The median score is a measure of location, so both
addition and multiplying the median score of test B by 40 and adding 50
to the result will affect the median score of test A.
Thus, the median score of test A is given by 40(26) + 50 = 1,090
Therefore, the median score of test A is 1,090.
Part F:
The IQR is a measure of spread, so multiplying the IQR of test B by 40 will affect the IQR but adding 50
to the result will not affect the IQR of test A.
Thus, the IQR of test A is given by 40(6) = 240
Therefore, the IQR of test A is 240.
