Answer:
Linear correlation exists
Step-by-step explanation:
Given the data :
X : | 2 4 5 6
Y : | 6 9 8 10
Using technology to fit the data and obtain the correlation Coefficient of the regression model,
The Correlation Coefficient, r is 0.886
To test if there exists a linear correlation :
Test statistic :
T = r / √(1 - r²) / (n - 2)
n = number of observations
T = 0.886 / √(1 - 0.886²) / (4 - 2)
T = 0.866 / 0.3535845
T = 2.449
Comparing Pvalue with α
If Pvalue < α ; Reject H0
Pvalue = 0.1143
α = 0.05
Pvalue > α ; We reject the null and conclude that linear correlation exists
Answer:
the amount left to spend is $740.77
Step-by-step explanation:
The computation of the amount left to spend is as follows:
= Each month payment - last month payment bills
= $2,185.76 - $73.49 - $897.19 - $474.31
= $740.77
Hence, the amount left to spend is $740.77
We simply deduct three amount bills from the each month payment
Answer:
No
Step-by-step explanation:
Because 10 - 6.79 = 3.21
Answer:
b. more money
Step-by-step explanation:
Randy's original position lets him make:
50*500 = $25,000
The new position will let him make:
$27,500
Therefore,
original position < new position = $25,000 < $27,500
Answer:
B. A teacher compares the pre-test and post-test scores of students
Step-by-step explanation:
the answer is true, because it is a good example to compare the tests between students, we know that a matching pair design is a random model and is used when the experiment allows grouping subjects in pairs based on a variable and each pair will receive randomly a different handling, the answer A is not true because in the example all students are uniformly averaged and the variable is not correlated with subgroups, option c is incorrect because the variable was not randomized and generates classification bias and the option d is incorrect because the teacher compares a small sample as her class with a score of a total sample, but does not intervene on her students when selecting the corresponding group
