Question

Choose the appropriate test to run (one-sample z, t-test, ANOVA, linear regression, or chi-square). What test did you choose and why is it appropriate for the dataset

Run the relevant statistical test. Insert your results from the chosen test (included in 5 pts above).

Is this test statistically significant? How did you make this determination

Trt. Calorie Intake

1. 435.16

1. 338.99

1. 488.73

1. 590.28

1. 582.59

1. 635.21

1. 249.86

1 441.66

1 572.43

1 357.78

1 396.79

1 298.38

1 282.99

1 368.51

1 388.59

1 256.32

1 408.82

1 424.94

1 477.96

1 428.74

1 432.52

1 428.27

1 596.79

1 456.3

1 446.38

2 414.61

2 503.46

2 425.22

2 288.77

2 184

2 299.73

2 350.65

2 394.94

2 261.55

2 295.28

2 139.69

2 462.78

2 179.59

2 301.75

2 436.58

2 371.39

2 469.02

2 378.09

2. 287.31

2. 448.55

2. 332.64

2. 403.98

1 = children participated in meal preparation
2 = children didn't participate in meal preparation

Answer 1

The t-test is the appropriate test because we do not know the population standard deviation.

The hypothesis being tested is:

H0: µ1 = µ2

H1: µ1 ≠ µ2

participated	didn't participate
431.3996	346.7991	mean
105.7012	99.5011	std. dev.
25	22	n
	45	df
	84.60051	difference (participated - didn't participate)
	10,579.02288	pooled variance
	102.85438	pooled std. dev.
	30.06702	standard error of difference
	0	hypothesized difference
	2.814	t
	.0072	p-value (two-tailed)

The p-value is 0.0072.

Since the p-value (0.0072) is less than the significance level (0.05), we can reject the null hypothesis.

Therefore, we can conclude that µ1 ≠ µ2.