CHYS 3P15 Lecture Notes - Lecture 4: Null Hypothesis

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violetoyster687

7 Nov 2017

School

Brock University

Department

Child and Youth Studies

Course

CHYS 3P15

Professor

D Molnar

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Document Summary

Use t tests to compare 1 sample mean to the mean of the population. Want to know if brock university students are lonelier than the average young adults. Null hypothesis: there are no differences between brock university students" lo(cid:374)eli(cid:374)ess s(cid:272)ores and the a(cid:448)erage you(cid:374)g adults" lo(cid:374)eli(cid:374)ess s(cid:272)ores. If the sig. value is less that 0. 05, reject the null hypothesis. If you want to compare this year, your performance based on your lab. If you (cid:449)a(cid:374)t to k(cid:374)o(cid:449) (cid:449)hether the la(cid:271) s(cid:272)ores are differe(cid:374)t (cid:271)ased o(cid:374) 5 differe(cid:374)t (cid:373)ea(cid:374)s . Null hypothesis: there is no differences between the loneliness scores of the 5 labs. Analyze compare means independent-samples t test test variable (loneliness) grouping (sex) The closer the groups are in size, the better. Mean loneliness scores for women vs. men (appears that women are more lonely than men but because there are (cid:373)ore of the(cid:373) i(cid:374) the group )

Related Questions

1. You are given only three quarterly seasonal indices and quarterly seasonally adjusted data for the entire year. What is the raw data value for Q4? Raw data is not adjusted for seasonality.

Quarter Seasonal Index Seasonally Adjusted Data

Q1 .80 295

Q2 .85 299

Q3 1.15 270

Q4 --- 271

2. One model of exponential smoothing will provide almost the same forecast as a liner trend method. What are linear trend intercept and slope counterparts for exponential smoothing?

A. Alpha and Delta

B. Delta and Gamma

C. Alpha and Gamma

D. Standard Deviation and Mean

3. When performing correlation analysis what is the null hypothesis? What measure in Minitab is used to test it and to be 95% confident in the significance of correlation coefficient.

A. Ho: r = .05 p < .5

B. Ho: r = 0 p >.05

C. Ho: r ? 0 p?.05

D. Ho: r = 0 p?.05

In decomposition what does the cycle factor (CF) of .80 represent for a monthly forecast estimate of a Y variable?

A. The estimated value is 80% of the average monthly seasonal estimate.
B. The estimate is .80 of the forecasted Y trend value.
C. The estimated value is .80 of the historical average CMA values.
D. The estimated value has 20% more variation than the average historical Y data values.

5. A Wendy's franchise owner notes that the sales per store has fallen below the stated national Wendy's outlet average of $1,368,000. He asserts a change has occurred that reduced the fast food eating habits of Americans. What is his hypothesis (H1) and what type of test for significance must be applied?

A. H1: u ? $1,368,000 A one-tailed t-test to the left.
B. H1: u = $1,368,000 A two-tailed t-test.
C. H1: u < $1,368,000 A one-tailed t-test to the left.
D. H1: p < $1,368,000 A one-tailed test to the right

6. As the sample size from a population increases, for a given level of significance what happens to the null hypothesis rejection region and size of the t-table value?

A. The rejection region and the t-table value generally gets smaller for sample size below 31.
B. The rejection region gets larger and the t-table value generally gets smaller for sample sizes below 31.
C. The rejection region remains unchanged while the t-table value gets smaller for all sample sizes.
D. The zero mean hypothesis region gets larger and the t-table value gets larger as well for sample sizes below 31.

7. You obtained autocorrelation LBQ value of 18.58 for the 12^th lag from a data sample. Are the data significantly correlated at the lag examined or not? You want to be 95% confident in your answer.

A. Yes. The data are significantly correlated through the 12th lag.
B. No. The data are not significantly correlated through the 12^th lag.

C. No. Only the 12 lag period is not correlated.

D. You cannot tell since the number of sample observations is not provided.

E. The p-value is above .05 so the data is correlated.

8. Sometimes forecasters get lazy or forgetful and do not check the significance of XY data correlations and use the X variable to forecast Y. What is the result of this?

A. Type 2 error
B. Autocorrelation error
C. Type 3 error
D. Type 1 error

9. Do error measures indicate the statistical significance of forecast model variables?

A. Yes. They move in the same direction as statistical significance.
B. Yes. As error measures decrease the variable significance increases.
C. No They indicate only the magnitude of estimate error.
D. No. They indicate only the statistical significance of a forecast or fitted values.

10. In exponential smoothing what is the weight of the alpha coefficient for a time series data observation from the 3^rd previous period if the original alpha value is set at .8?

A. The weight cannot be calculated since the data observation is not given.
B. The weight is zero since the alpha value is set relatively high.
C. .548
D. .0064

11.Given the data series below for variables Y (Monthly Inventory Balance) and X (Monthly Sales) are they significantly correlated at the 95% confidence level and how can you tell?

Ending Inv. Bal. Y		Monthly Sales X
1544		5053
1913		5052
2028		7507
1178		2887
1554		3880
1910		4454
1208		3855
2467		8824
2101		5716

A. Yes. The correlation coefficient is .873 that is greater than .05.
B. Yes. The correlation p-value is .002 which is less than .05.
C. No. The correlation coefficient is above the p-value.
D.No. The correlation p-value is greater than the 95% confidence level.

12. You have forecast the sales for your company for the last 12 months and the forecast residuals are shown below. Are these residuals to be considered random?
Residuals

-24

-348

-892

-62

-378

-489

-342

490

578

198

A. Yes, since the residuals randomly vary in magnitude.
B. Yes since the residuals are positive and negative and vary in magnitude.
C. No, since the residuals are stationary and vary in magnitude.
D. No, since the residuals indicate positive slope.

13. Given the residuals in problem 12 above what is the RMSE for the forecast?

A. -101.0
B. 411.8
C. 169603
D. 220.1

14. Which form of exponential smoothing can result in a na

Please answer the question after reading the paragraph.

We have a global health challenge in our hands today, and that is that the way we currently discover and develop new drugs is too costly, takes far too long, and it fails more often than it succeeds. It really just isn't working, and that means that patients that badly need new therapies are not getting them, and diseases are going untreated. We seem to be spending more and more money. So for every billion dollars we spend in R&D, we're getting less drugs approved into the market. More money, less drugs. Now we have a whole pipeline of different organ chips that we are currently working on in our labs. Now, the true power of this technology, however, really comes from the fact that we can fluidically link them. There's fluid flowing across these cells, so we can begin to interconnect multiple different chips together to form what we call a virtual human on a chip. Now we're really getting excited.We're not going to ever recreate a whole human in these chips, but what our goal is is to be able to recreate sufficient functionality so that we can make better predictions of what's going to happen in humans. For example, now we can begin to explore what happens when we put a drug like an aerosol drug. Those of you like me who have asthma, when you take your inhaler, we can explore how that drug comes into your lungs, how it enters the body, how it might affect, say, your heart. Does it change the beating of your heart? Does it have a toxicity? Does it get cleared by the liver? Is it metabolized in the liver? Is it excreted in your kidneys? We can begin to study the dynamic response of the body to a drug. Organs on chips could also change the way we do clinical trials in the future. Right now, the average participant in a clinical trial is that: average. Tends to be middle aged, tends to be female. You won't find many clinical trials in which children are involved, yet every day, we give children medications, and the only safety data we have on that drug is one that we obtained from adults. Children are not adults. They may not respond in the same way adults do. There are other things like genetic differences in populations that may lead to at-risk populations that are at risk of having an adverse drug reaction. Now imagine if we could take cells from all those different populations, put them on chips, and create populations on a chip. This could really change the way we do clinical trials. And this is the team and the people that are doing this. We have engineers, we have cell biologists, we have clinicians, all working together. We're really seeing something quite incredible at the Wyss Institute. It's really a convergence of disciplines, where biology is influencing the way we design, the way we engineer, the way we build. It's pretty exciting.

Question: For years, technology and medicine have joined together to pave the way or even solve some of the worldâs greatest issues in healthcare. These issues include but are not limited to incurable diseases and disorders like AIDS and cancer. According to Geraldine Hamilton however, we as a population are spending too much money to develop drugs and solutions to these issues, only to be at a deficit with the drugs actually produced. She believes that one of the keys is how we test these drugs and finding more efficient ways to do so. As a result, she suggests the use of organs on chips, an innovation created and pioneered by her lab. The use of organs on chips could change the way new drugs are released and tested, thus possibly furthering the process of creating solutions to healthcareâs problems. Where is the fine line between technology and human innovation? Is human innovation gradually becoming depending on technology? (At least two paragraphs, a paragraph is at least 5 sentences long.)

CHYS 3P15 Lecture Notes - Lecture 4: Null Hypothesis

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