...ONE WAY ANOVA One-way analysis of variance (abbreviated one-way ANOVA) is a technique used to compare means of two or more samples (using the F distribution). This technique can be used only for numerical data. The ANOVA tests the null hypothesis that samples in two or more groups are drawn from populations with the same mean values. To do this, two estimates are made of the population variance. These estimates rely on various assumptions. The ANOVA produces an F-statistic, the ratio of the variance calculated among the means to the variance within the samples. If the group means are drawn from populations with the same mean values, the variance between the group means should be lower than the variance of the samples, following the central limit theorem. A higher ratio therefore implies that the samples were drawn from populations with different mean values. Descriptives | | N | Mean | Std. Deviation | Std. Error | 95% Confidence Interval for Mean | Minimum | Maximum | | | | | | Lower Bound | Upper Bound | | | QUALITY | 1 | 19 | 3.89 | .809 | .186 | 3.50 | 4.28 | 2 | 5 | | 2 | 12 | 3.83 | .937 | .271 | 3.24 | 4.43 | 1 | 5 | | Total | 31 | 3.87 | .846 | .152 | 3.56 | 4.18 | 1 | 5 | PRICE | 1 | 19 | 2.95 | .911 | .209 | 2.51 | 3.39 | 1 | 5 | | 2 | 12 | 2.75 | 1.055 | .305 | 2.08 | 3.42 | 1 | 5 | | Total | 31 | 2.87 | .957 | .172 | 2.52 | 3.22 | 1 | 5 | BRAND | 1 | 19 | 4.11 | .809 | .186 | 3.72 | 4.50 | 3 | 5 | | 2 | 12 | 4.17 | .577 | .167...
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...No-show rates range between 15% to 30% in an ambulatory setting and lead to wasted resources, increased financial burdens and inaccurate or missed diagnoses of patients (Goldman et al., 1982). Previous studies have shown that various patient factors can predict future no-show behavior. For example, the type of appointment scheduled for a patient can predict patient absenteeism (Zeber, Pearson, & Smith, 2009). Zeber et al. found that colonoscopy appointments are the most commonly missed appointments (Zeber et al., 2009). Furthermore, previous missed appointments is one of the most significant predictors of no-show appointments (Dove & Schneider, 1981). Studies have also shown that patients’ various psychosocial diagnoses are indicators of missed appointments (Goldman et al., 1982). Patients diagnosed with at least one psychological diagnosis, including mood disorders, such as depression and bipolar disease, anxiety disorders, such as panic attacks and posttraumatic stress disorder, and thought disorders, such as schizophrenia and personality disorders, were more likely to miss appointments compared to patients without psychological diagnoses (Savageau et al., 2004). Finally, Perron et al. showed that patients with substance abuse disorders are more likely to miss appointments (Perron et al., 2010). In order to reduce no-show rates in a hospital gastrointestinal (GI) clinic this project analyzed potential indicators of missed appointments. Based on a conceptual model grouping...
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...Case 14.1 1. Correlations | | Prefer Drive Less than 30 Minutes | Prefer Unusual Desserts | Prefer Large Variety of Entrees | Prefer Unusual Entrees | Prefer Drive Less than 30 Minutes | Pearson Correlation | 1 | .768** | .806** | .765** | | Sig. (2-tailed) | | .000 | .000 | .000 | | N | 400 | 400 | 400 | 400 | Prefer Unusual Desserts | Pearson Correlation | .768** | 1 | .823** | .868** | | Sig. (2-tailed) | .000 | | .000 | .000 | | N | 400 | 400 | 400 | 400 | Prefer Large Variety of Entrees | Pearson Correlation | .806** | .823** | 1 | .831** | | Sig. (2-tailed) | .000 | .000 | | .000 | | N | 400 | 400 | 400 | 400 | Prefer Unusual Entrees | Pearson Correlation | .765** | .868** | .831** | 1 | | Sig. (2-tailed) | .000 | .000 | .000 | | | N | 400 | 400 | 400 | 400 | **. Correlation is significant at the 0.01 level (2-tailed). | Null Hypothesis- No relation between preference to drive 30 minutes or less and preference of menu items Alternative Hypothesis- There is a relation between the preference to drive 30 minutes or less and preference of menu items Interpretation-All the correlations have sig values that are significantly different from zero. So, we reject the null hypothesis. The correlations are positive and they are in the moderate range. As the preference to drive 30 minutes or less increases, so do preferences for unusual deserts, large variety of entrees, and unusual entrees. Correlations | | Prefer Drive Less than 30 Minutes | Prefer...
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...Cross tabulation Analysis | | | | | | | | | | | | | | | | | | | | | | | | | | CROSSTABS VARIABLES ANALYZED | | | | | | | Row Variable ->> | Do you use Friendly Market regularly | | | | | | Column Variable ->> | I always pay cash. | | | | | | | | | | | | | | | | Observed Frequencies | | | | | | | | | Disagree | Neutral | Agree | Grand Total | | Statistical Values | No | 9 | 62 | 19 | 90 | | Chi Sq | df | Sig | Yes | 16 | 39 | 17 | 72 | | 5.38 | 2 | 0.07 | Grand Total | 25 | 101 | 36 | 162 | | | | | | | | | | | | | | There is NO significant association between these two variables. | | | | | (95% level of confidence) | | | | | | | | In total of 162 populations provide the answer for both questions, in those 17 peoples agrees the statement, 19 peoples not agreeing the statement, in total of 101 peoples giving neutral answers ,in that 62 peoples agrees the statement, 39 peoples not agreeing the statement. Using the chi square calculation, chi square values is 5.38, with degree of freedom 2, the significance of chi square value is 0.077 so, the null hypothesis is true therefore probability of 0.077(0.077%) case payment in the friendly market either in cash or credit card. Recommendation: The customer does not care about the mode of payment. Cross tabulation Analysis | | | | | | | | | | | | | | | | | |...
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...graphs can determine the mode, Box & Whiskers does not. g) T F The birth data from the Anaheim Ducks and Los Angeles Kings proved Outliers was correct. h) T F For the Hypergeometric distribution the value of p changes each time an object is selected. i) T F Heights of adult males is a good example of the Poisson distribution. j) T F When children give their age, it’s continuous; for adults it’s integer. k) T F If a LUMAT template cell is colored, you can enter data or labels. l) T F The Box and Whiskers template gives indicators of data being normal, uniform or exponential. m) T F Goodness of Fit templates use the Chi-square distribution to give the probability of a fit. n) LUMAT stands for: Learning to Use Managerial Analysis Templates. o) The name of our Excel Training program is ExcelEverest. p) If the pieces of a pie chart in Excel add up to only...
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...Experimental Design and Analysis of Variance Review: chi square = we want to know whether a data set fits a certain distribution/independence model. We use the chi square distribution, then we check how far away the test statistic is from 0. As data set becomes farther away from what you expect to get, you get larger differences between expected model and actual model (you get a larger test statistic) Components of ANOVA: Factor – independent variable. We want this variable to be qualitative. Classifications of the factor is called the treatments. (ex. Color of the light vs. response variable ie height of the plant. Light is qualitative, treatments are the kinds of lights such ash red, white, violet, green. In anova, the response variable must be quantitative. If not quantitative, then go back to chi square test) When we design an experiment, the factors are controlled by you. But sometimes some factors are difficult to control, and if we want to do an experiment on that we will have to just look at observational data. Example of this kind of factor is the weather. Regardless, usually to test whether a certain factor has an effect on a response variable, we do replication. We look at replicating the experiment on more units. The more the better. If we find differences between the growths (in the mongo seeds) we do not know if this is true for the whole population, so the more elements of sample we have the better. Gasoline Mileage Case: Factor: Gas Type. Treatments:...
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...4/7/2014 Basic Statistics: An Overview Basic Statistics: Review Descriptive Statistics Scatter graph Measures of central tendency Mean Median, quartile, deciles, percentile Mode Weighted mean GM HM Measures of dispersion Range, IQR Semi IQR Mean deviation Standard deviation Variance Coeff of variation Inferential Statistics Populations Sampling Estimation of Parameters Point Estimation Interval Estimation Unbiased Minimum Variance Consistency Efficiency Properties of Point Estimators Statistical Inference: Hypothesis Testing T test F test Chi square test Measures of shape of the curve Moments Skewness kurtosis Probability distributions Normal Distribution T-student Distribution Chi-Square Distribution F Distribution Index Number Etc. Correlational Statistics Covariance Correlations regressions 1 4/7/2014 Some Terminology Variables are things that we measure, control, or manipulate .They may be classified as: 1. Quantitative i.e. numerical Continuous: takes fractional values ex. height in cm Discrete : takes no fractional values ex. GDP Random Variable: If the value of a variable cannot be predicted in advance Non random : If the value of a variable cannot be predicted in advance Some Terminology 2. Qualitative i.e. non numerical 1. Nominal: Items are usually categorical and may have numbers...
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...Customer’s Perception on Automotive Spare Parts at XYZ Limited*, Kolkata Suprotim Ganguly (PGDM No.: 12113) Student, SDMIMD, Mysore suprotim12113@sdmimd.ac.in Nilanjan Sengupta Professor-HRM, SDMIMD, Mysore nilanjan@sdmimd.ac.in 41 Comments by the Faculty The XYZ group comprises of over 100 operating companies in seven business sectors: communications and information technology, engineering, materials, services, energy, consumer products and chemicals. The group has operations in more than 80 countries across six continents, and its companies export products and services to 85 countries. The project work has been carried out in the Bearings division. The project is essentially focused on Consumer Involvement theory which basically means how the consumer is involved in the purchase of various products in the market and how the consumer is responding towards the product after purchasing the product. The objectives of this study were to study and analyze the consumer decision making process with respect to the purchase and usage of automobile spares, and, to recommend factors which should be included in the Integrated Marketing Communication. It has been revealed that, the Indian Bearing market is also growing at a much rapid pace. It was also indentified that, the most of the factors considered in the study have significant effect on the overall satisfaction of the customers. It is recommended that surveys should become a regular feature of every company so that changes in the...
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...Today insurance industry is one of the most growing sectors in India. There is lot of potential in the Indian Insurance Industry. There are many issues, which require study. The scope of the study of insurance industry of India would be very great as there are ongoing developments in the industry after the opening of the sector. The present study has been undertaken to identify the association between demographics of individual investors and their investment behavior and also analyzing the acceptance of insurance by them. The study was conducted using the survey method (Personal interviews and Telephonic interviews). Data was collected through a structured questionnaire from a sample of 150 respondents from Delhi. Factor and regression analysis and Cross Tabs was used to analyze the data and identify the effect of customers’ perception about the quality of performance of various factors on customer satisfaction. INTRODUCTION In India insurance is a flourishing industry, with several national and international players competing to excel. With several reforms and policy regulations, the Indian insurance sector has witnessed tremendous growth in the recent past. Insurance can be defined as a...
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...Session Nine (Lab): Cluster Analysis MART 307 Assignment Four: Cluster Analysis 1. T When looking at the Agglomeration Schedule for Wards linkage for the last 10 clusters, the difference between coefficients of stage 162 and 16(Cluster #2) is 352.72. The difference between the coefficients of stage 161 and 160(Cluster#3) is 304.538. The difference between the coefficients of stage 160 and 159(Cluster#4) is 177.043. When looking at the chart, there is a biggest jump between clusters 3 and 4, indicating that there is a biggest difference between those two clusters. This is backed up by the Dendrogram as shown to the left, when putting a straight line through the longest horizontal lines; the line is cut by three clusters. Also, when looking at the Ward Scree Plot, the biggest kink is at 3 as shown by the arrow above which shows an abrupt change in angle (elbow.) Which indicates the 3rd cluster being more unique than the forth. The single linkage message also shows we should use 3 clusters, because looking at the Dendrogram, if we put a line through the longest horizontal distances it would be cut at 3 points. I would choose Wards method over Single Linkage because it is much clearer, the dendogram has much clearer clusters and there are fewer clusters. The agglomeration schedule is easier to figure out 2) 1 means not at all considered 2 unlikely to consider 3 would possibly consider 4 would actively consider 5 already do As shown in the Initial Cluster...
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...relationship is (strong, moderate, weak) o Relationships should be assessed in this order How to Analyze Relationships 1. Choose variables to analyze 2. Determine if the variables are interval/ratio or nominal/ordinal 3. Use the correct relationship analysis a. For two interval/ratio variables – use correlation b. For two nominal/ordinal variables – use cross-tabs 4. Does a relationship exist? 5. If relationship exists, determine the direction a. Monotonic will be increasing/decreasing b. Nonmonotonic will be looking for a pattern 6. Assess the strength of relationship a. With correlation – size of coefficient denotes the strength b. With cross-tabs – the pattern is assessed Cross-Tabulations and Chi Square • Cross-tabulations o Consists of rows and columns defined by the categories classifying each variable. Used for nonmonotonic relationships o Sometimes referred to as an “r x c” table (rows x columns) ▪ Crosstabulation cell – intersection of a row and a column o Interested in inner cells to determine relationship before statistically testing ▪ Use the chi-square for statistical tests o Tables consist of four types of numbers in each cell: ▪ Frequency ▪ Raw percentage ▪ Column percentage ▪ Row percentage o When we have two nominal-scaled variables...
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...Lack of Consumer Awareness about Processed Food Items ABSTRACT In India, frequently customers purchase food items without being fully aware of the negative side-effects of the ingredients because of lack of knowledge and under the influence of clever marketing strategies. This research has been taken up on “Noodles” which contains Mono Sodium Glutamate (MSG). Exploratory research methodology was adopted with primary data collected mostly in Bangalore. Our research reveals that there is a large ignorance about the possible negative side effects of MSG. Surprisingly many say that they will continue to purchase the items because they are not convinced. The research is of great importance for consumer protection. It should help the authorities in formulating suitable guideline for product promotion. KEY WORDS: Consumer ignorance, Food, Noodle, MSG Introduction Customers are either consumers themselves or purchasers on behalf of the consumers. It is nothing but natural to assume that customers purchase products which are best suited for them and what they consider value for money. However, there can be situations when a customer buys a product without being fully aware of the pros and cons of using the particular product. This may be trivial for a simple purchase of low value item. But it can be particularly serious in case of food item. The question is - are all customers well aware of the negative effects of such products? Who should educate the...
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...Information Systems and Technology Trends Occurring in the Workplace Nancy M. Gonzenbach Southern Illinois University Carbondale Information Systems and Technology Trends Occurring in the Workplace Introduction The advancement of technologies in business is changing the world of work. Businesses find they must incorporate many of the new technological procedures, processes, policies, hardware, and software into their environment to remain competitive. The impact from these technological advances on how work is performed affects the workforce far differently now than what has been seen over the past 25 years. One of the greatest impacts has been on the workers themselves, and the challenge of training and preparing to remain abreast in today’s workforce is tremendous. This challenge, which is facing today’s workers, is something educators cannot afford to ignore. Technology has created a revolution in today’s educational environment, and this change in education makes teaching more rewarding and far more challenging (Bryant, 2001). Review of Literature The field of information systems is constantly changing, and these changes are impacting the way workers think, the processes and procedures used to accomplish wo rk, and the tools used to accomplish goals needed to be competitive in the workforce. In today’s work environment “trends such as the globalization of the economy and the evolution of e-commerce are changing the very nature of work” and “success in the wo rkplace of...
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...found the answer. ii. Give a URL or Stata manual-with-page-number or Stata help window or data-analysis-book-with-page-number (or the equivalent) that contains the answer. iii. Run the appropriate command and include the command and Stata output in your write-up. For some of the questions, a graph is produced, and should also be included in your write-up for this assignment. a) How can the Stata pwcorr command be used to find the significance level (i.e., p-value) for the test that the correlation between the variables salary and market is zero? i. Before beginning, we performed a Google search to better understand the correlation between variables. After loading the data file, we then entered the “pwcorr salary market, sig print (.05)” command. The University of Indiana Stata website was very helpful in both providing code and explanations. In this case, the correlation between salary and market test scores was positive, moderately strong, and statistically significant (r=.4072, p < .001). To find the significance level ii. http://kb.iu.edu/data/alya.html iii. The Stata code and results are: b) How can one use the Stata tabulate command to perform a chi square test of independence between variables admin and male? To perform the analogous Fisher’s exact test? i. We performed a Google search for the tabulate command and found our answer for both the Chi Squared and Fisher’s exact test on...
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...CHI-SQUARE TEST - ANALYSIS OF CONTINGENCY TABLES David C. Howell University of Vermont The term ”chi-square” refers both to a statistical distribution and to a hypothesis testing procedure that produces a statistic that is approximately distributed as the chi-square distribution. In this entry the term is used in its second sense. PEARSON’S CHI-SQUARE The original chi-square test, often known as Pearson’s chi-square, dates from papers by Karl Pearson in the earlier 1900s. The test serves both as a ”goodnessof-fit” test, where the data are categorized along one dimension, and as a test for the more common ”contingency table”, in which categorization is across two or more dimensions. Voinov and Nikulin, this volume, discuss the controversy over the correct form for the goodness of fit test. This entry will focus on the lack of agreement about tests on contingency tables. In 2000 the Vermont State legislature approved a bill authorizing civil unions. The vote can be broken down by gender to produce the following table, with the expected frequencies given in parentheses. The expected frequencies are computed as Ri × Cj /N, where Ri and Cj represent row and column marginal totals and N is the grand total. Vote Women Men Total Yes 35 (28.83) 60 (66.17) 95 No 9 (15.17) 41 (34.83) 50 Total 44 101 145 The standard Pearson chi-square statistic is defined as χ2 = (Oij − Eij )2 (35 − 28.83)2 (41 − 34.83)2 = + ··· + = 5.50 Eij 28.83 34.83 where i and j index the rows and columns of...
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