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Created: Nov 14, 2018
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Lab ADHD
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1.

Aud before- The correlation value is 0.992 which is higher than our critical value (0.987). We can infer that the data is normally distributed.

Aud after-The correlation value is 0.995 which is higher than our critical value (0.987). We can infer that the data is normally distributed.

Vis before- The correlation value is 0.994 which is higher than our critical value (0.987). We can infer that the data is normally distributed.

Vua after- The correlation value is 0.997 which is higher than our critical value (0.987). We can infer that the data is normally distributed.

According to our QQ plot, all of the data is normally distributed. 

2. I used a hypothesis test to determine if  H0; U=600. HA;> 600.

Our given alpha value is 0.05. After testing our hypothesis, i found the P value to be 0.0016. P value is less than alpha, therefor we reject the null hypothesis. There is not sufficent evidence to conclude that HA; U>600.

 3.At this time, there is not sufficent evidence to conclude that the medication has helped. We will reject the hypothesis because the P value is less than the alpha value.

4.

There's not a large variance between auditory before and after in the given data. This can be interpreted as, the responce has not decreased. Meaning, the effect of the trial did not play a significant role in the "after auditory".

5.

There is enough evidence to conclude that this is unusually low. Considering out P value was less than our alpha value. 

6.

There is not enough information collected from the before medication data to conclude that the mean time is greater than 440. In this case we would reject our hypothesis because p value is less than alpha.

7. Finally, we are able to conclude that there is enough evidence to support the statement that the medication is improving visual response times. We would reject the hypothesis because our p value is now 0.0017 which is smaller than our alpha value (0.05).

Result 1: QQ Plot # 9   [Info]
Right click to copy

Result 2: T hypothesis test ADHD #2   [Info]
One sample T hypothesis test:
μ : Mean of variable
H0 : μ = 600
HA : μ > 600

Hypothesis test results:
VariableSample MeanStd. Err.DFT-StatP-value
Aud-Before640.0165313.3067491203.00723570.0016

Result 3: Two sample T hypothesis test ADHD #3   [Info]
Two sample T hypothesis test:
μ1 : Mean of Aud-Before
μ2 : Mean of Aud-After
μ1 - μ2 : Difference between two means
H0 : μ1 - μ2 = 0
HA : μ1 - μ2 > 0
(without pooled variances)

Hypothesis test results:
DifferenceSample Diff.Std. Err.DFT-StatP-value
μ1 - μ291.87252915.41853194.242515.958579<0.0001

Result 4: Two sample variance hypothesis test #4   [Info]
Two sample variance hypothesis test:
σ12 : Variance of Aud-Before
σ22 : Variance of Aud-After
σ1222 : Ratio of two variances
H0 : σ1222 = 0
HA : σ1222 > 0

Hypothesis test results:
RatioNum. DFDen. DFSample RatioF-StatP-value
σ12221201242.8255698Infinity<0.0001

Result 5: One sample proportion hypothesis test #5   [Info]
One sample proportion hypothesis test:
Outcomes in : Aud-Before
Success : 121
p : Proportion of successes
H0 : p = 0.5
HA : p < 0.5

Hypothesis test results:
VariableCountTotalSample Prop.Std. Err.Z-StatP-value
Aud-Before012100.045454545-11<0.0001

Result 6: One sample T hypothesis test #6 A   [Info]
One sample T hypothesis test:
μ : Mean of variable
H0 : μ = 440
HA : μ > 440

Hypothesis test results:
VariableSample MeanStd. Err.DFT-StatP-value
Vis-Before385.540327.6389944123-7.12916841

Result 7: Two sample T hypothesis test #7   [Info]
Two sample T hypothesis test:
μ1 : Mean of Vis-Before
μ2 : Mean of Vis-After
μ1 - μ2 : Difference between two means
H0 : μ1 - μ2 = 0
HA : μ1 - μ2 > 0
(without pooled variances)

Hypothesis test results:
DifferenceSample Diff.Std. Err.DFT-StatP-value
μ1 - μ228.7177429.7118425232.878712.95698180.0017

HTML link:
<A href="https://www.statcrunch.com/5.0/viewreport.php?reportid=82365">Lab ADHD</A>

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