Aresearcher was intecosted in seeing if there was a signilcant dillere.pdf

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Aresearcher was intecosted in seeing if there was a signilcant dillerense betwoen Ohio Univerity undergraduate and graduble students in lema of the average zumber of hours that they stidy on the weekend. So, he collected data troen two random samples (below) Use aphay m os. x. State tin mal and atemative thpotheces in uaget. ( 2 poins) ti. Det io the coteria be mating a seivion. That in, fre the offoa value ( i panti) E Consva the acorophale lesi usiaile. sHow youR woredi Beate complete try Nor noding the ymianct! (3 pousts) Compute the appropriate test-statistic. SHOW YOUR WOAKI Pleaso complete by NOT pooling the variancel (3 poinss) Calciated Numerator = Cavovaled Denominator = Calculated Final value = d. Evaluate the null typothesis (based on your answen to the above stepe). (Crow One) PEECT FML TO REHET (1 point e. Bined on your evaluation of the cill hypothais, what ia your condusice? (1 point) the cos 2 (3 points).

Education

This document provides an overview of hypothesis testing, analysis of variance (ANOVA), and how to properly quote references and include a bibliography. It discusses the key steps in hypothesis testing, including stating the null and alternative hypotheses, choosing a significance level, determining the sampling distribution, calculating probabilities, and deciding whether to reject or fail to reject the null hypothesis. It also outlines one-way and two-way ANOVA, explaining how to calculate variances between and within samples/groups and use an F-test statistic. Finally, it defines what a bibliography is, lists standard citation styles, and distinguishes between references cited in a work and a full bibliography.

CHAPTER I- Part 1.pptx

JaysonMagalong

Anova.pptx

Ravindra Nath Shukla

The document provides an overview of analysis of variance (ANOVA), including what it is, how it works, key terminology, and the steps to conduct one-way and two-way ANOVA tests. ANOVA is a statistical technique used to test if there are significant differences between the means of two or more groups. It compares the variation within groups to the variation between groups to determine if observed differences are due to chance. The document outlines the null and alternative hypotheses, calculations for sums of squares, degrees of freedom, F-statistics, and how to interpret the results against critical values from the F-distribution table.

Random variable

Leamel Sarita

The document is a prayer asking God for guidance and blessings in one's studies and work. It asks God to pour forth brilliance on the intellect to dissipate darkness of sin and ignorance. It asks for a penetrating mind to understand, a retentive memory, ease in learning, lucidity to comprehend, and grace in expressing oneself. It asks God to guide the beginning, direct the progress, and bring the work to completion, through Jesus Christ.

A study on the ANOVA ANALYSIS OF VARIANCE.pptx

jibinjohn140

ANOVA (analysis of variance) is a statistical method used to test if the means of three or more samples or groups are equal. It divides the total variation in a data set into variation between groups and variation within groups. An F-test is used to compare the ratio of between-group variation and within-group variation. If the F-calculated value is less than the F-critical value, the null hypothesis that the sample means are equal is accepted. ANOVA can test for differences between more than two groups which makes it more efficient than multiple t-tests.

Multiple sample test - Anova, Chi-square, Test of association, Goodness of Fit

Rupak Roy

Quality Engineering material

TeluguSudhakar3

This document provides an overview of one-way analysis of variance (ANOVA). It begins by explaining the basic concepts and settings for ANOVA, including comparing population means across three or more groups. It then covers the hypotheses, ideas, assumptions, and calculations involved in one-way ANOVA. These include splitting total variability into parts between and within groups, computing an F-statistic to test if population means are equal, and potentially performing multiple comparisons between pairs of groups if the F-test is significant. Worked examples are provided to illustrate key ANOVA concepts and calculations.

Correlation: Bivariate Data and Scatter Plot

DenzelMontuya1

This document discusses bivariate data and scatter plots. Bivariate data involves collecting values of two variables from the same population. A scatter plot can show the relationship between two quantitative variables by plotting their ordered pairs. The direction and strength of the pattern in a scatter plot indicates whether the variables have a positive, negative, or no correlation. The correlation coefficient (r) measures the strength of the linear relationship between variables on a scale from -1 to 1.

EPS 525 – Introduction to Statistics Assignment No. 5 – One-way Analysis of Variance Name: A researcher conducted a study to examine the effects of secure, anxious, and avoidant attachment styles on the physiology of sleep. Participants were selected using a stratified random sampling approach to ensure representation of each of the three styles. The sleep patterns of 30 secure, 30 anxious, and 30 avoidant children were monitored. Of primary importance to the researcher was the overall percentage of time each child spent in deep (delta) sleep. Following is the average amount of time that each child spent in delta sleep, expressed as a percentage of total sleep time (ranging from 0.0 to 100.0). For the attachment styles, 1 = secure, 2 = anxious, and 3 = avoidant. Data Output for this Assignment is found on the last four pages. The gray boxes for your answers will expand as necessary for your responses. 1. (2 points) What would the null hypothesis be for this study? Show/write the appropriate symbols and expression in words. H0: . 2. (2 points) What would the alternative hypothesis be for this study? Show/write the appropriate symbols and expression in words. Ha: 3. Prior to examining whether the group means differ; you need to test the underlying assumptions of the one-way analysis of variance. 3.a. (2 points) Has the assumption of independence been met for this data? FORMCHECKBOX Yes FORMCHECKBOX No (check your answer selection) Indicate how you made your determination. 3.b. (3 points) Has the assumption of normality been met for this data, using an alpha level of .001? FORMCHECKBOX Yes FORMCHECKBOX No (check your answer selection) Indicate how you made your determination. Be sure to include all applicable values and symbols. 3.c. (3 points) Has the assumption of homogeneity of variance been met for this data, using an alpha level of .05? That is, is this assumption met or not met? FORMCHECKBOX Yes FORMCHECKBOX No (check your answer selection) Indicate how you came to your conclusion. Be sure to include all applicable values and symbols. 4. (2 points) The next question that needs to be answered is whether all of the groups are the same in their percentage of time in deep (delta) sleep using an alpha level of .05. If applicable (or indicate why not), use the Welch statistic. What is your conclusion (at this point) from this analysis? Indicate how you came to your conclusion. Be sure to include all applicable values and symbols. 5. (3 points) Calculate the measure of association and interpret its meaning. W T W B MS SS MS K SS + - - = ) 1 ( 2 wWhere SSB = K = MSW = SST = MSW = Therefore, (2 = This means: 6. (1 point) Write the statistical strand for this one-way ANOVA analysis. 7. (4 points) Assuming that you found a significant F, which pairs of groups differ? Indicate which post hoc procedure you used and why. Indicate your findings fr ...

10.Analysis of Variance.ppt

AbdulhaqAli

The document provides an overview of analysis of variance (ANOVA) including its purpose, assumptions, computations, and applications. It explains that ANOVA tests whether population means are equal by comparing two estimators of variance - the variation between sample means and the variation within samples. If the null hypothesis that all population means are equal is true, the between-sample variation will be small relative to the within-sample variation. The document outlines the computations and formulas behind ANOVA including definitions of terms like treatment deviations, error deviations, and sums of squares. It also describes how to interpret and report ANOVA results including the F-statistic and ANOVA table.

A computer supplies retailer wishes to open a store in one of two .docx

evonnehoggarth79783

Chapter 11 Chi-Square Tests and ANOVA 359 Chapter .docx

bartholomeocoombs

Chapter 11: Chi-Square Tests and ANOVA 359 Chapter 11: Chi-Square and ANOVA Tests This chapter presents material on three more hypothesis tests. One is used to determine significant relationship between two qualitative variables, the second is used to determine if the sample data has a particular distribution, and the last is used to determine significant relationships between means of 3 or more samples. Section 11.1: Chi-Square Test for Independence Remember, qualitative data is where you collect data on individuals that are categories or names. Then you would count how many of the individuals had particular qualities. An example is that there is a theory that there is a relationship between breastfeeding and autism. To determine if there is a relationship, researchers could collect the time period that a mother breastfed her child and if that child was diagnosed with autism. Then you would have a table containing this information. Now you want to know if each cell is independent of each other cell. Remember, independence says that one event does not affect another event. Here it means that having autism is independent of being breastfed. What you really want is to see if they are not independent. In other words, does one affect the other? If you were to do a hypothesis test, this is your alternative hypothesis and the null hypothesis is that they are independent. There is a hypothesis test for this and it is called the Chi-Square Test for Independence. Technically it should be called the Chi-Square Test for Dependence, but for historical reasons it is known as the test for independence. Just as with previous hypothesis tests, all the steps are the same except for the assumptions and the test statistic. Hypothesis Test for Chi-Square Test 1. State the null and alternative hypotheses and the level of significance Ho : the two variables are independent (this means that the one variable is not affected by the other) HA : the two variables are dependent (this means that the one variable is affected by the other) Also, state your α level here. 2. State and check the assumptions for the hypothesis test a. A random sample is taken. b. Expected frequencies for each cell are greater than or equal to 5 (The expected frequencies, E, will be calculated later, and this assumption means E ≥ 5 ). 3. Find the test statistic and p-value Finding the test statistic involves several steps. First the data is collected and counted, and then it is organized into a table (in a table each entry is called a cell). These values are known as the observed frequencies, which the symbol for an observed frequency is O. Each table is made up of rows and columns. Then each row is totaled to give a row total and each column is totaled to give a column total. Chapter 11: Chi-Squared Tests and ANOVA 360 The null hypothesis is that the variables are independent. Using the multiplication.

Goodness of-fit

Long Beach City College

The document discusses goodness-of-fit tests which are used to determine if observed frequency counts fit a claimed distribution. It provides examples of how to calculate expected frequencies under different assumptions and how to perform a chi-square goodness-of-fit test. The test statistic is calculated as the sum of the squared differences between observed and expected frequencies divided by the expected frequencies. The test statistic is then compared to critical values from the chi-square distribution to determine if there is a significant lack of fit. Examples demonstrate how to state hypotheses, calculate test statistics, compare to critical values, and make conclusions about goodness of fit.

Parametric & non parametric

ANCYBS

This document provides an overview of parametric and non-parametric statistical tests. Parametric tests assume the data follows a known distribution (e.g. normal) while non-parametric tests make no assumptions. Common non-parametric tests covered include chi-square, sign, Mann-Whitney U, and Spearman's rank correlation. The chi-square test is described in more detail, including how to calculate chi-square values, degrees of freedom, and testing for independence and goodness of fit.

One-Way ANOVA

Long Beach City College

16885618.ppt

ArfanAhmed22

The runs test can be used to analyze a series of events for randomness. It involves separating a sequence into runs of the same event and counting the number of runs. For small sample sizes, the number of runs is compared to critical values in a table to determine if it falls within or outside the critical region, indicating whether to reject or fail to reject the null hypothesis of randomness. Examples provided demonstrate applying the runs test to analyze whether a teacher called on students randomly by gender and whether a student's quiz scores were random.

Graphical presentation of data

jennytuazon01630

This document discusses different types of graphs that can be used to present data graphically: pie charts, bar graphs, histograms, frequency polygons, and cumulative frequency polygons. It also discusses stem and leaf plots and dot plots. Additionally, it covers measures of central tendency including the mean, median, and mode. It provides examples of calculating each measure and includes sample problems for students to practice finding the mean, median, and mode.

Lesson-1-Random-Variable-for-quiz-1.pptx

ronniedelacruz12

This document defines key concepts related to random variables including discrete and continuous random variables. It explains that a random variable is a variable whose possible values are determined by chance. Discrete random variables have a finite number of possible values that are usually counts, while continuous random variables have an infinite number of possible values that are typically measurements. The document also discusses how to determine if a random variable is discrete or continuous and how to find the possible values of a random variable from a given experiment or situation.

Descriptive Statistics Formula Sheet Sample Populatio.docx

simonithomas47935

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

EXPLORING-RANDOM-VARIABLES.pptx

Pauline Misty Panganiban

This document contains a summary of key concepts in statistics: - Statistics involves collecting and analyzing data to make inferences about populations. It has two broad areas: descriptive statistics which organizes data, and inferential statistics which makes conclusions about populations based on samples. - Variables can be either qualitative (represent categories) or quantitative (represent numerical amounts). Examples of each are given. - Random variables assign numerical values to outcomes of experiments. They can be either discrete (countable outcomes) or continuous (measured on a scale). Examples of each are provided.

Answer SheetISDS 361BHW5YOUR FULL NAME Answer.docx

rossskuddershamus

Answer Sheet ISDS 361B HW5 YOUR FULL NAME: Answer Optional for Partial Credit 1. [12 pts.] Forecast for Saturday in week 6: No. of Periods of Data Collected = No. of Periods in Season = MSE = MAD = MAPE = LAD = 2. [18 pts.] a. Forecast (exponential smoothing): No. of Periods of Data Collected = Smoothing Constant (alpha) = Initial Forecast Value = b. Forecast (weighted MA): MSE = MAD = MAPE = LAD = c. I would recommend weighted MA (True/ False): 3. [12 pts.] Forecast for year 8: MSE = MAD = MAPE = LAD = 4. [18 pts.] Forecast for Summer Year 5: No. of Periods of Data Collected = No. of Periods in Season = Coefficients: Intercept = Period = 1 = 2 = Page 1 of 2 1) What is the critical F value for a sample of 7 observations in the numerator and 6 in the denominator? Use a two-tailed test and the 0.1 significance level. (Round your answer to 2 decimal places.) F 2) Arbitron Media Research Inc. conducted a study of the iPod listening habits of men and women. One facet of the study involved the mean listening time. It was discovered that the mean listening time for men was 29 minutes per day. The standard deviation of the sample of the 10 men studied was 8 minutes per day. The mean listening time for the 12 women studied was also 29 minutes, but the standard deviation of the sample was 15 minutes. At the .10 significance level, can we conclude that there is a difference in the variation in the listening times for men and women? (Round your answer to 3 decimal places.) The test statistic is . Decision 3) The following is sample information. Test the hypothesis that the treatment means are equal. Use the 0.01 significance level. Treatment 1 Treatment 2 Treatment 3 7 4 4 4 5 7 6 5 6 6 4 5 (a) State the null hypothesis and the alternate hypothesis. H0 H1 (b) What is the decision rule? (Round your answer to 2 decimal places.) H0 if the test statistic is greater than . (c&d) Compute SST, SSE, and SS total and complete an ANOVA table. (Round SS, MS and F values to 3 decimal places.) Source SS df MS F Treatments Error Total (e) State your decision regarding the null hypothesis. H0 4) A senior accounting major at Midsouth State University has job offers from four CPA firms. To explore the offers further, she asked a sample of recent trainees how many months each worked for the firm before receiving a raise in salary. The sample information is submitted to MINITAB with the following results: Analysis of Variance Source DF SS MS F Factor 5 36.39 7.28 1.92 Error 12 45.54 3.80 Total 17 81.93 Reject if F > . (Round your answer to 2 .

Statistics

Bob Smullen

This document provides an overview of key concepts related to data in biology including: 1. Qualitative and quantitative data types. Qualitative data relates to characteristics or descriptions while quantitative data uses numerical scales. 2. Methods for displaying and analyzing data including graphs, measures of central tendency (mean, median, mode), and standard deviation. 3. Statistical hypothesis testing using t-tests to compare two samples and determine if differences are statistically significant. 4. Correlation and scatter plots which show the relationship between two variables but do not prove causation.

36086 Topic Discussion3Number of Pages 2 (Double Spaced).docx

rhetttrevannion

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 .

SWOT AnalysisHCS499 Version 32University of Phoenix Mater.docx

ssuserf9c51d

SWOT Analysis HCS/499 Version 3 2 University of Phoenix Material SWOT Analysis Based on review of the performance analysis of Stevens District Hospital, consider what you perceive to be the strengths, weaknesses, opportunities and threats for this hospital. · Strengths and weaknesses are traits internal to the hospital, i.e. strong physician loyalty to hospital, aging building, and availability of financial resources. · Opportunities and threats are external to the hospital, such as a mall facility available for lease or a competitor hospital opening two physician practices in your market. SWOT Analysis Review the SWOT Analysis PowerPoint® prior to completing this assignment. Based on the review of the Stevens District Hospital strategic planning scenario, conduct a SWOT analysis to generate a list of perceived strengths, weaknesses, opportunities, and threats for the hospital. · Strengths and weaknesses are traits internal to the hospital (i.e., strong physician loyalty to hospital, aging building, and availability of financial resources). · Opportunities and threats are external to the hospital, such as a mall facility available for lease or a competitor hospital opening two physician practices in your market. Write a 700- to 1,050-word analysis that incorporates the key components of a SWOT analysis for the scenario described in Week One to generate a list of perceived strengths, weaknesses, opportunities, and threats. The analysis will include the following: · Analyze the purpose of conducting the analysis in the context of the scenario. · Analyze the limitations and advantages of conducting a SWOT analysis on your own (vs. with a group of stakeholders). · Use the table provided to record your analysis of the information from the strategic planning scenario and generate two factors for each of the SWOT categories (strengths, weaknesses, opportunities, and threats). Cite at least 1 peer-reviewed, scholarly, or similar references to support your assignment. Click the Assignment Files tab to submit your assignment. Table 1: SWOT Analysis Analysis of existing information from Stevens District Hospital strategic planning scenario Perceived strength (internal) Analysis of existing information from Stevens District Hospital strategic planning scenario Perceived strength (internal) Analysis of existing information from Stevens District Hospital strategic planning scenario Perceived weakness (internal) Analysis of existing information from Stevens District Hospital strategic planning scenario Perceived weakness (internal) Analysis of existing information from Stevens District Hospital strategic planning scenario Perceived opportunity (external) Analysis of existing information from Stevens District Hospital strategic planning scenario Perceived opportunity (external) Analysis of existing information from Stevens District Hospital strategic planning scenario Perceived threat (external) Analysis of existing informa ...

Analysis of variance (ANOVA)

Sneh Kumari

This document discusses various types of analysis of variance (ANOVA) statistical tests. It begins with an introduction to one-way ANOVA for comparing the means of three or more independent groups. Requirements for one-way ANOVA include a nominal independent variable with three or more levels and a continuous dependent variable. Assumptions of one-way ANOVA include normality and homogeneity of variances. The document then briefly discusses two-way ANOVA, MANOVA, ANOVA with repeated measures, and related statistical tests. Examples of each type of ANOVA are provided.

ANOVA.ppt

JahangirBaigSeniorLe

The document discusses analysis of variance (ANOVA) and experimental design. It explains that ANOVA allows testing the hypothesis of equality of multiple population means simultaneously, rather than conducting multiple pairwise t-tests. It describes the components of an ANOVA table including sources of variation (treatments, error), degrees of freedom, sums of squares, and mean squares. An example compares the yields of three potato varieties in a completely randomized design and demonstrates forming the ANOVA table to analyze the data.

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Aresearcher was intecosted in seeing if there was a signilcant dillere.pdf

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