Question 12

Null Null Hypothesis Theoretical non-null value Any mean Type Il
 error Alternative Hypothesis HI Type I error

HYPOTHESIS TESTING OUTCOMES Reality The Null Hypothesis Is True
 Accurate Type I Error h The Null Hypthesis Is True The Alternative
 Hypothesis is True The Alternative Hypothesis is True Type Il Error
 Accurate O

Type I error, also known as a "false positive": the error of
 rejecting a null hypothesis when it is actually frue. In other words,
 this is the error of accepting an alternative hypothesis (the real
 hypothesis of interest) when the results can be attributed to chance.
 Plainly speaking, it occurs when we are observing a difference when in
 truth there is none (or more specifically - no statistically
 significant difference). So the probability of making a type I error
 in a test with rejection region R is P(R I Ho is true) . Type Il
 error, also known as a "false negative": the error of not rejecting a
 null hypothesis when the alternative hypothesis is the true state of
 nature. In other words, this is the error of failing to accept an
 alternative hypothesis when you don't have adequate power. Plainly
 speaking, it occurs when we are failing to observe a difference when
 in truth there is one. So the probability of making a type Il error in
 a test with rejection region R is 1 — P(R I Ha is true). The power of
 the test can be P(R IHa is true) .

Question 15

What Does 95% Confidence Mean Anyway? • A 95% confidence interval
 means that the method used to construct the interval will produce
 intervals containing the true p in about 95% of the intervals
 constructed. • This means that if the 95% Cl method was used in 100
 different samples, we would expect that about 95 of the intervals
 would contain the true p, and about 5 intervals would not contain the
 true p. Spo kane Virtual Learning Excellence for Everyone. Fv.wwher,
 Everyday

Question 22

Sampling Distribution Variable Categorical (example: left-handed or
 not) Quantitative (example: age) Parameter P = population proportion g
 = population mean, o = population standard deviation Statistic D —
 sample proportion x = sample Center Spread PCI — p) Shape Normal IF np
 10 and n(l -p) 10 When will the distribution of sample means be
 approximately normal

Question 23

  • 95% confidence interval of a sampling

    2% 3% 4% 5% Sample size = 2,401 Margin of error = 2% Sample size =
1,067 Margin of error = 3% Sample size = 600 Margin of error = 4%
Sample size = 384 Margin of error = 5% Sample size = 96 Margin of
error =10% 2,401 1,067 600 384 96

  • Margin of error vs. sample size at different confidence level

    Confidence Level Sample 150 200 250 350 450 500 550 600 650 700 750
850 900 950 1000 % Margin of % Margin of Error 5.3 3.0 2.9 2.7 2.2 2.0
Error 5.2 3.9 3.0 2.8 2.7 2.7 % Margin of 9.8 6.2 5.7 5.2 3.8

  • Equation

    • C:\\6432CA65\\FE01530B-89BD-4F8B-A3E1-55F12080AD12\_files\\image464.png

    • C:\\6432CA65\\FE01530B-89BD-4F8B-A3E1-55F12080AD12\_files\\image465.png

Question 25

Standard error of difference • To compare two groups, we have to
 calculate the standard difference error between the 1- groups
 Distribution Parameter Population value Sample estimate Normal
 Binomial Poisson mean Proportion Rate Tt1-Tt2 PI-P2 Standard error
 -PI) P2(1-P2

Question 26

  • C:\\6432CA65\\FE01530B-89BD-4F8B-A3E1-55F12080AD12\_files\\image467.png

Question 27

(row total) (column total) (grand total)

One rainy Saturday morning, Adam woke up to hear his mom complaining
 about the house being dirty. "Mom is always grouchy when it rains,"
 Adam's brother said to him. So Adam decided to figure out if this
 statement was actually true- For the next year, he charted every time
 it rained and every time his mom was grouchy. What he found was very
 interesting - rainy days and his mom being grouchy were entirely
 independent events\_ Some of his data are shown in the table below.
 Fill in the missing values from the frequency table. Grouchy Not
 grouchy Column total Raining 35 Not reining 26 H 330 Row total 73 292
 365 muc.Åv

Question 30

In engineering, science, and statistics, replication is the
 repetition of an experimental condition so that the variability
 associated with the phenomenon can be estimated. ASTM, in standard
 E1847, defines replication as "the repetition of the set of all the
 treatment combinations to be compared in an experiment. Replication
 (statistics) - Wikipedia
 https://en.wikipedia.org/Wiki/Replication\_(statistics)

Question 33

Inferences about the Slope: Confidence Interval Example Confidence
 Interval Estimate of the Slope: Excel Printout for Produce Stores
 Lower 95% Upper 95% Intercept 475.810926 2797.01853 X Variable
 1.06249037 1.91077694 At 95% level of confidence the confidence
 interval for the slope is (1.062, 1.911). Does not include O. 0 20
 Conclusion: There is a significant linear dependency annual sales on
 the size of the store. unap 1 U-4

  • C:\\6432CA65\\FE01530B-89BD-4F8B-A3E1-55F12080AD12\_files\\image472.png

Question 38

"Blocking" vs "stratification" 'Blocking" • word used in describing
 an experimental design "Stratification" • used in describing a survey
 or observational study • Both refer to idea of only making comparisons
 within relatively similar groups of subjects Chapter 6 8

Question 40

  • Original data

    0.00 0.13 0.26 0.39 0.52 0.65 0.78 0.91

  • Sampling distribution of the sample mean with sample size = 2

    0.00 0.13 0.26 0.39 0.52 0.65 0.78 0.91

results matching ""

    No results matching ""