Assume that you have a sample of n1=8, with the sample mean X1=47, an.pdf

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Assume that you have a sample of n1=8, with the sample mean X1=47, and a sample standard deviation of S1=7, and you have an independent sample of n2=17 from another population with a sample mean of X2=37 and the sample standard deviation S2=6. Complete parts (a) through (d) below. a. What is the value of the pooled-variance tSTAT test statistic for testing H0:1=2 ? tSTAT= (Round to two decimal places as needed.) first and 150 for the people who thought about the organic fruit salad first. Complete parts (a) through (e) below. B. A Type I error is committed if the null hypothesis is rejected but the mean estimate is significantly lower for the people who thought about the cheesecake. C. A Type I error is committed if the alternative hypothesis is rejected but the mean estimate is not significantly lower for the people who thought about the cheesecake. D. A Type I error is committed if one concludes that the mean estimate is not significantly lower for the people who thought about the cheesecake when it is significantly lower. c. In the context of this study, what is the meaning of a Type II error? A. A Type II error is committed if the null hypothesis is rejected but the mean estimate is not significantly lower for the people who thought about the cheesecake. D. A Type II error is committed if the alternative hypothesis is rejected but the mean estimate is significantly lower for the people who thought about the cheesecake. thought about the organic fruit salad first? Find the test statistic. tSSTA= (Round to two decimal places as needed.) Find the p-value. p-value = (Round to three decimal places as needed.).

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roman_numerals_buggy/package.bluej #BlueJ package file dependency1.from=RomanNumeralsTest dependency1.to=RomanNumerals dependency1.type=UsesDependency package.editor.height=400 package.editor.width=560 package.editor.x=733 package.editor.y=118 package.numDependencies=1 package.numTargets=2 package.showExtends=true package.showUses=true target1.editor.height=700 target1.editor.width=900 target1.editor.x=623 target1.editor.y=216 target1.height=50 target1.name=RomanNumeralsTest target1.naviview.expanded=false target1.showInterface=false target1.type=UnitTestTarget target1.width=140 target1.x=70 target1.y=70 target2.editor.height=700 target2.editor.width=900 target2.editor.x=578 target2.editor.y=92 target2.height=50 target2.name=RomanNumerals target2.naviview.expanded=false target2.showInterface=false target2.type=ClassTarget target2.width=120 target2.x=70 target2.y=10 roman_numerals_buggy/README.TXT ------------------------------------------------------------------------ This is the project README file. Here, you should describe your project. Tell the reader (someone who does not know anything about this project) all he/she needs to know. The comments should usually include at least: ------------------------------------------------------------------------ PROJECT TITLE: PURPOSE OF PROJECT: VERSION or DATE: HOW TO START THIS PROJECT: AUTHORS: USER INSTRUCTIONS: roman_numerals_buggy/RomanNumerals.classpublicsynchronizedclass RomanNumerals { public void RomanNumerals(); public String toRoman(int); } roman_numerals_buggy/RomanNumerals.ctxt #BlueJ class context comment0.params=n comment0.target=java.lang.String\ toRoman(int) numComments=1 roman_numerals_buggy/RomanNumerals.javaroman_numerals_buggy/RomanNumerals.javapublicclassRomanNumerals { publicString toRoman(int n){ String r =""; while( n >0){ if(n>=1000){ r +="M"; n -=1000; }elseif( n >500){ r +="D"; n -=500; }elseif(n>=100){ r +="C"; n -=100; }elseif(n>=50){ r +="L"; n -=50; }elseif(n >=10){ r +="X"; n -=10; }elseif(n >=5){ r +="V"; n -=5; }else{ r +="I"; n -=1; } } return r; } } roman_numerals_buggy/RomanNumeralsTest.classpublicsynchronizedclass RomanNumeralsTest extends junit.framework.TestCase { public void RomanNumeralsTest(); protected void setUp(); protected void tearDown(); public void test_1(); public void test_3(); public void test_8(); public void test_27(); public void test_2011(); public void test_44(); public void test555(); public void test500(); } roman_numerals_buggy/RomanNumeralsTest.ctxt #BlueJ class context comment0.params= comment0.target=RomanNumeralsTest() comment0.text=\r\n\ Default\ constructor\ for\ test\ class\ RomanNumeralsTest\r\ ...

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36086 Topic: Discussion3 Number of Pages: 2 (Double Spaced) Number of sources: 1 Writing Style: APA Type of document: Essay Academic Level:Master Category: Psychology Language Style: English (U.S.) Order Instructions: Attached I will upload the instructions Reference/Module Learning Objectives •Explain what the x2 goodness-of-fit test is and what it does. •Calculate a x2 goodness-of-fit test. •List the assumptions of the x2 goodness-of-fit test. •Calculate the x2 test of independence. •Interpret the x2 test of independence. •Explain the assumptions of the x2 test of independence. The Chi-Square (x2) Goodness-of-Fit test: What It Is and What It Does The chi-square (x2) goodness-of-fit test is used for comparing categorical information against what we would expect based on previous knowledge. As such, it tests what are called observed frequencies (the frequency with which participants fall into a category) against expected frequencies (the frequency expected in a category if the sample data represent the population). It is a nondirectional test, meaning that the alternative hypothesis is neither one-tailed nor two-tailed. The alternative hypothesis for a x2 goodness-of-fit test is that the observed data do not fit the expected frequencies for the population, and the null hypothesis is that they do fit the expected frequencies for the population. There is no conventional way to write these hypotheses in symbols, as we have done with the previous statistical tests. To illustrate the x2 goodness-of-fit test, let's look at a situation in which its use would be appropriate. chi-square (x2) goodness-of-fit test A nonparametric inferential procedure that determines how well an observed frequency distribution fits an expected distribution. observed frequencies The frequency with which participants fall into a category. expected frequencies The frequency expected in a category if the sample data represent the population. Calculations for the x2 Goodness-of-Fit Test Suppose that a researcher is interested in determining whether the teenage pregnancy rate at a particular high school is different from the rate statewide. Assume that the rate statewide is 17%. A random sample of 80 female students is selected from the target high school. Seven of the students are either pregnant now or have been pregnant previously. The χ2goodness-of-fit test measures the observed frequencies against the expected frequencies. The observed and expected frequencies are presented in Table 21.1. TABLE 21.1Observed and expected frequencies for χ2 goodness-of-fit example FREQUENCIES PREGNANT NOT PREGNANT Observed 7 73 Expected 14 66 As can be seen in the table, the observed frequencies represent the number of high school females in the sample of 80 who were pregnant versus not pregnant. The expected frequencies represent what we would expect based on chance, given what is known about the population. In this case, we would expect 17% of the females to be pregnant .

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Int 150 “The Moral Instinct” 1. Most cultures agree that abusing innocent people is wrong. True or false 2. Young children have a sense of morality. True or false (example)? 3. Emotional reasoning trumps rationalizing. True or false (explain) 4. According to the article, psychopathy or moral misbehavior (like rape) is more environmental than genetic. True or false (example) 5. Explain the point about the British schoolteacher in Sudan. 6. Name three things anthropologists believe all people share, in addition to thinking it’s bad to harm others and good to help them. a. b. c. 7. What is reciprocal altruism? 8. How does the psychologist Tetlock explain the outrage of American college students at the thought that adoption agencies should place children with couples willing to pay the most? 9. Discuss: A love for children and sense of justice is just an expression of our innate sense of preserving our genes for future generations (Darwin) 10. What does the author warn about the arguments regarding climate change? Hypothesis Testing (Statistical Significance) 1 Hypothesis Testing Goal: Make statement(s) regarding unknown population parameter values based on sample data Elements of a hypothesis test: Null hypothesis - Statement regarding the value(s) of unknown parameter(s). Typically will imply no association between explanatory and response variables in our applications (will always contain an equality) Alternative hypothesis - Statement contradictory to the null hypothesis (will always contain an inequality) The level of significant (Alpha) is the maximum probability of committing a type I error. P(type I error)= alpha Definitions Rejection (alpha, α) Region: Represents area under the curve that is used to reject the null hypothesis Level of Confidence, 1 - alpha (a): Also known as fail to reject (FTR) region Represents area under the curve that is used to fail to reject the null hypothesis FTR H0 α/2 α/2 3 1 vs. 2 Sided Tests Two-sided test No a priori reason 1 group should have stronger effect Used for most tests Example H0: μ1 = μ2 HA: μ1 ≠ μ2 One-sided test Specific interest in only one direction Not scientifically relevant/interesting if reverse situation true Example H0: μ1 ≤ μ2 HA: μ1 > μ2 4 Example: It is believed that the mean age of smokers in San Bernardino is 47. Researchers from LLU believe that the average age is different than 47. Hypothesis H0:μ = 47 HA: μ ≠ 47 μ = 47 α /2 = 0.025 Fail to Reject (FTR) α /2 = 0.025 5 Three Approaches to Reject or Fail to Reject A Null Hypothesis: 1a. Confidence interval Calculate the confidence interval Decision Rule: a. If the confidence interval (CI) includes the null, then the decision must be to fail to reject the H0. b. If the confidence interval (CI) does not include the null, then the decision must be to reject the H0. 6 1b. Confidence interval to compare groups Calculate the confidence interval for each gro.

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Descriptive Statistics Formula Sheet Sample Population Characteristic statistic Parameter raw scores x, y, . . . . . X, Y, . . . . . mean (central tendency) M = ∑ x n μ = ∑ X N range (interval/ratio data) highest minus lowest value highest minus lowest value deviation (distance from mean) Deviation = (x − M ) Deviation = (X − μ ) average deviation (average distance from mean) ∑(x − M ) n = 0 ∑(X − μ ) N sum of the squares (SS) (computational formula) SS = ∑ x 2 − (∑ x)2 n SS = ∑ X2 − (∑ X)2 N variance ( average deviation2 or standard deviation 2 ) (computational formula) s2 = ∑ x2 − (∑ x)2 n n − 1 = SS df σ2 = ∑ X2 − (∑ X)2 N N standard deviation (average deviation or distance from mean) (computational formula) s = √∑ x 2 − (∑ x)2 n n − 1 σ = √∑ X 2 − (∑ X)2 N N Z scores (standard scores) mean = 0 standard deviation = ± 1.0 Z = x − M s = deviation stand. dev. X = M + Zs Z = X − μ σ X = μ + Zσ Area Under the Normal Curve -1s to +1s = 68.3% -2s to +2s = 95.4% -3s to +3s = 99.7% Using Z Score Table for Normal Distribution (Note: see graph and table in A-23) for percentiles (proportion or %) below X for positive Z scores – use body column for negative Z scores – use tail column for proportions or percentage above X for positive Z scores – use tail column for negative Z scores – use body column to discover percentage / proportion between two X values 1. Convert each X to Z score 2. Find appropriate area (body or tail) for each Z score 3. Subtract or add areas as appropriate 4. Change area to % (area × 100 = %) Regression lines (central tendency line for all points; used for predictions only) formula uses raw scores b = slope a = y-intercept y = bx + a (plug in x to predict y) b = ∑ xy − (∑ x)(∑ y) n ∑ x2 − (∑ x)2 n a = My - bMx where My is mean of y and Mx is mean of x SEest (measures accuracy of predictions; same properties as standard deviation) Pearson Correlation Coefficient (used to measure relationship; uses Z scores) r = ∑ xy− (∑ x)(∑ y) n √(∑ x2− (∑ x)2 n )(∑ y2− (∑ y)2 n ) r = degree x & 𝑦 𝑣𝑎𝑟𝑦 𝑡𝑜𝑔𝑒𝑡ℎ𝑒𝑟 degree x & 𝑦 𝑣𝑎𝑟𝑦 𝑠𝑒𝑝𝑎𝑟𝑎𝑡𝑒𝑙𝑦 r 2 = estimate or % of accuracy of predictions PSYC 2317 Mark W. Tengler, M.S. Assignment #9 Hypothesis Testing 9.1 Briefly explain in your own words the advantage of using an alpha level (α) = .01 versus an α = .05. In general, what is the disadvantage of using a smaller alpha level? 9.2 Discuss in your own words the errors that can be made in hypothesis testing. a. What is a type I error? Why might it occur? b. What is a type II error? How does it happen? 9.3 The term error is used in two different ways in the context of a hypothesis test. First, there is the concept of sta

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