Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
This document discusses various numerical descriptive techniques used for summarizing and describing quantitative data, including:
- Measures of central location (mean, median, mode) and how to calculate them
- Measures of variability (range, variance, standard deviation) and how they are used to quantify the dispersion of data around the mean
- Other concepts like percentiles, the empirical rule, Chebyshev's theorem, and box plots. Examples are provided to illustrate how to apply these techniques to sample data sets.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
Research Method for Business chapter 11-12-14Mazhar Poohlah
This document provides guidance on determining appropriate sample sizes based on population size. It states that for populations under 100, the entire population should be surveyed. For populations around 500, a sample size of 50% is recommended, while for populations around 1,500, a sample size of 20% is recommended. Beyond a population of 5,000, a sample size of 400 may be adequate regardless of total population size. The document also provides a table comparing strengths and weaknesses of different sampling techniques, including probability and non-probability methods.
Descriptive statistics helps users to describe and understand the features of a specific dataset, by providing short summaries and a graphic depiction of the measured data. Descriptive Statistical algorithms are sophisticated techniques that, within the confines of a self-serve analytical tool, can be simplified in a uniform, interactive environment to produce results that clearly illustrate answers and optimize decisions.
Factor analysis is a technique used to reduce a large set of variables down to a smaller set of underlying factors or components. It can identify hidden constructs or dimensions in a set of data that may not be obvious. For example, when a bank asks customers many questions, factor analysis can group related characteristics like dependability, honesty and reliability into a single "trustworthiness" factor. It provides a concise representation of the data while also identifying interrelationships between variables.
Predictive analytics targets data to predict if ATL advertising is more effective than BTL advertising and to target customer segments and characteristics.
Dear students get fully solved assignments by professionals
Send your semester & Specialization name to our mail id :
stuffstudy5@gmail.com
or
call us at : 098153-33456
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
This document discusses various numerical descriptive techniques used for summarizing and describing quantitative data, including:
- Measures of central location (mean, median, mode) and how to calculate them
- Measures of variability (range, variance, standard deviation) and how they are used to quantify the dispersion of data around the mean
- Other concepts like percentiles, the empirical rule, Chebyshev's theorem, and box plots. Examples are provided to illustrate how to apply these techniques to sample data sets.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
Research Method for Business chapter 11-12-14Mazhar Poohlah
This document provides guidance on determining appropriate sample sizes based on population size. It states that for populations under 100, the entire population should be surveyed. For populations around 500, a sample size of 50% is recommended, while for populations around 1,500, a sample size of 20% is recommended. Beyond a population of 5,000, a sample size of 400 may be adequate regardless of total population size. The document also provides a table comparing strengths and weaknesses of different sampling techniques, including probability and non-probability methods.
Descriptive statistics helps users to describe and understand the features of a specific dataset, by providing short summaries and a graphic depiction of the measured data. Descriptive Statistical algorithms are sophisticated techniques that, within the confines of a self-serve analytical tool, can be simplified in a uniform, interactive environment to produce results that clearly illustrate answers and optimize decisions.
Factor analysis is a technique used to reduce a large set of variables down to a smaller set of underlying factors or components. It can identify hidden constructs or dimensions in a set of data that may not be obvious. For example, when a bank asks customers many questions, factor analysis can group related characteristics like dependability, honesty and reliability into a single "trustworthiness" factor. It provides a concise representation of the data while also identifying interrelationships between variables.
Predictive analytics targets data to predict if ATL advertising is more effective than BTL advertising and to target customer segments and characteristics.
Dear students get fully solved assignments by professionals
Send your semester & Specialization name to our mail id :
stuffstudy5@gmail.com
or
call us at : 098153-33456
Business research measurement and scalingNishant Pahad
This document discusses various methods of measurement and scaling that are used in business research. It describes unidimensional and multidimensional scales, and different types of scales including nominal, ordinal, interval, and ratio scales. It then covers specific scaling techniques such as paired comparison, constant sum, rank order, and continuous rating scales. It also discusses evaluating scales based on criteria like generalizability, validity, reliability, and sensitivity.
Measurement and scaling noncomparative scaling techniqueRohit Kumar
This chapter discusses noncomparative scaling techniques used in marketing research to measure attitudes, perceptions, and preferences. It describes continuous rating scales where respondents place a mark on a line, and itemized rating scales like the Likert scale, semantic differential, and Stapel scale where respondents select a category. The chapter also covers evaluating scales based on their reliability, validity, and generalizability.
Generalized Linear Regression with Gaussian Distribution is a statistical technique which is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. The Generalized Linear Model (GLM) generalizes linear regression by allowing the linear model to be related to the response variable via a link function (in this case link function being Gaussian Distribution) and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.
Chp9 - Research Methods for Business By Authors Uma Sekaran and Roger BougieHassan Usman
This document discusses measurement scales and establishing the reliability and validity of measures. It describes the four main types of scales - nominal, ordinal, interval, and ratio - and provides examples of each. Rating and ranking scales are introduced as ways to develop measures using these scales. The document emphasizes the importance of establishing the reliability of measures through assessing stability and internal consistency, as well as validity, to ensure the measures accurately capture the concepts they are intended to. Item analysis, reliability testing, and validity assessment are presented as key ways to evaluate the quality of developed measures.
The document discusses different types of measurement scales used in research including nominal, ordinal, interval, and ratio scales. It provides examples of each scale and the types of numerical operations that can be performed on data for each scale. Nominal scales involve simple sorting into categories while ratio scales allow for absolute comparisons between values. The document also covers various rating scale formats researchers can use to measure attributes, including Likert scales, semantic differential scales, and graphic rating scales. Reliability and validity are discussed as important aspects of ensuring measurement instruments accurately measure the intended constructs.
Naive Bayes is a classification algorithm that is suitable for binary and multiclass classification. It is suitable for binary and multiclass classification. Naïve Bayes performs well in cases of categorical input variables compared to numerical variables. It is useful for making predictions and forecasting data based on historical results.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
The KMeans Clustering algorithm is a process by which objects are classified into number of groups so that they are as much dissimilar as possible from one group to another, and as much similar as possible within each group. This algorithm is very useful in identifying patterns within groups and understanding the common characteristics to support decisions regarding pricing, product features, risk within certain groups, etc.
This document provides an assignment for a statistics course. It contains 6 questions covering topics like descriptive statistics, probability, sampling, hypothesis testing, analysis of variance, and index numbers. Students are asked to answer the questions in approximately 400 words each. They are provided with the evaluation scheme and instructed to submit their answers via email or phone for review and feedback.
Random Forest Classification is a machine learning technique utilizing aggregated outcome of many decision tree classifiers in order to improve precision of the outcome. It measures the relationship between the categorical target variable and one or more independent variables.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
Isotonic Regression is a statistical technique of fitting a free-form line to a sequence of observations such that the fitted line is non-decreasing (or non-increasing) everywhere, and lies as close to the observations as possible. Isotonic Regression is limited to predicting numeric output so the dependent variable must be numeric in nature…
This document discusses various techniques for scaling and measurement in research, including:
1. Primary scales of measurement like nominal, ordinal, interval, and ratio scales.
2. Comparative scaling techniques like paired comparison, rank order, and constant sum scales that involve comparing objects.
3. Noncomparative or monadic scaling techniques like continuous and itemized rating scales that involve rating single objects like Likert, semantic differential, and Stapel scales.
4. Factors that influence measurement accuracy like true score, systematic error, and random error in the true score model.
Hierarchical Clustering is a process by which objects are classified into a number of groups so that they are as much dissimilar as possible from one group to another group and as similar as possible within each group. This technique can help an enterprise organize data into groups to identify similarities and, equally important, dissimilar groups and characteristics, so the business can target pricing, products, services, marketing messages and more.
"Multilayer perceptron (MLP) is a technique of feed
forward artificial neural network using back
propagation learning method to classify the target
variable used for supervised learning. It consists of multiple layers and non-linear activation allowing it to distinguish data that is not linearly separable."
QM0012- STATISTICAL PROCESS CONTROL AND PROCESS CAPABILITYsmumbahelp
This document provides information about a fully solved assignment for students. It lists the semester, specialization, subject code, name, credits, and marks. It also provides 6 questions related to statistical process control and process capability. For each question, it provides the evaluation scheme and space to write the answer. The questions cover topics like Pareto charts, scatter diagrams, Poisson distribution, hypothesis testing, analysis of variance, attribute control charts, and the methodology for statistical process control implementation.
This document provides information about assignment help available for various MBA subjects and semesters. Students can send their semester and specialization details to the provided email address or call the phone number to get fully solved assignments. The document then provides details of one such assignment on research methodology, including questions on topics like steps in research, exploratory research design, measurement scales, types of questionnaires, hypothesis testing, and structure of a research report. Guidelines are provided for writing effective research reports.
Validate data
Questionnaire checking
Edit acceptable questionnaires
Code the questionnaires
Keypunch the data
Clean the data set
Statistically adjust the data
Store the data set for analysis
Analyse data
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
Predictive Analytics for customer targeting identifies buying frequency, what causes customers to buy, factors informing purchases and messaging by segment.
This document contains summaries of answers to 6 questions about statistics concepts for a business management course. Question topics include: distinguishing between classification and tabulation; explaining arithmetic mean; calculating mean and standard deviation for combined data sets; expectations in a game of chance; probability of disease contraction among employees; applying a chi-squared test to determine if a population has decreased. Each answer is 1-2 paragraphs and refers the reader to an external site for full details.
Business research measurement and scalingNishant Pahad
This document discusses various methods of measurement and scaling that are used in business research. It describes unidimensional and multidimensional scales, and different types of scales including nominal, ordinal, interval, and ratio scales. It then covers specific scaling techniques such as paired comparison, constant sum, rank order, and continuous rating scales. It also discusses evaluating scales based on criteria like generalizability, validity, reliability, and sensitivity.
Measurement and scaling noncomparative scaling techniqueRohit Kumar
This chapter discusses noncomparative scaling techniques used in marketing research to measure attitudes, perceptions, and preferences. It describes continuous rating scales where respondents place a mark on a line, and itemized rating scales like the Likert scale, semantic differential, and Stapel scale where respondents select a category. The chapter also covers evaluating scales based on their reliability, validity, and generalizability.
Generalized Linear Regression with Gaussian Distribution is a statistical technique which is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. The Generalized Linear Model (GLM) generalizes linear regression by allowing the linear model to be related to the response variable via a link function (in this case link function being Gaussian Distribution) and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.
Chp9 - Research Methods for Business By Authors Uma Sekaran and Roger BougieHassan Usman
This document discusses measurement scales and establishing the reliability and validity of measures. It describes the four main types of scales - nominal, ordinal, interval, and ratio - and provides examples of each. Rating and ranking scales are introduced as ways to develop measures using these scales. The document emphasizes the importance of establishing the reliability of measures through assessing stability and internal consistency, as well as validity, to ensure the measures accurately capture the concepts they are intended to. Item analysis, reliability testing, and validity assessment are presented as key ways to evaluate the quality of developed measures.
The document discusses different types of measurement scales used in research including nominal, ordinal, interval, and ratio scales. It provides examples of each scale and the types of numerical operations that can be performed on data for each scale. Nominal scales involve simple sorting into categories while ratio scales allow for absolute comparisons between values. The document also covers various rating scale formats researchers can use to measure attributes, including Likert scales, semantic differential scales, and graphic rating scales. Reliability and validity are discussed as important aspects of ensuring measurement instruments accurately measure the intended constructs.
Naive Bayes is a classification algorithm that is suitable for binary and multiclass classification. It is suitable for binary and multiclass classification. Naïve Bayes performs well in cases of categorical input variables compared to numerical variables. It is useful for making predictions and forecasting data based on historical results.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
The KMeans Clustering algorithm is a process by which objects are classified into number of groups so that they are as much dissimilar as possible from one group to another, and as much similar as possible within each group. This algorithm is very useful in identifying patterns within groups and understanding the common characteristics to support decisions regarding pricing, product features, risk within certain groups, etc.
This document provides an assignment for a statistics course. It contains 6 questions covering topics like descriptive statistics, probability, sampling, hypothesis testing, analysis of variance, and index numbers. Students are asked to answer the questions in approximately 400 words each. They are provided with the evaluation scheme and instructed to submit their answers via email or phone for review and feedback.
Random Forest Classification is a machine learning technique utilizing aggregated outcome of many decision tree classifiers in order to improve precision of the outcome. It measures the relationship between the categorical target variable and one or more independent variables.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
Isotonic Regression is a statistical technique of fitting a free-form line to a sequence of observations such that the fitted line is non-decreasing (or non-increasing) everywhere, and lies as close to the observations as possible. Isotonic Regression is limited to predicting numeric output so the dependent variable must be numeric in nature…
This document discusses various techniques for scaling and measurement in research, including:
1. Primary scales of measurement like nominal, ordinal, interval, and ratio scales.
2. Comparative scaling techniques like paired comparison, rank order, and constant sum scales that involve comparing objects.
3. Noncomparative or monadic scaling techniques like continuous and itemized rating scales that involve rating single objects like Likert, semantic differential, and Stapel scales.
4. Factors that influence measurement accuracy like true score, systematic error, and random error in the true score model.
Hierarchical Clustering is a process by which objects are classified into a number of groups so that they are as much dissimilar as possible from one group to another group and as similar as possible within each group. This technique can help an enterprise organize data into groups to identify similarities and, equally important, dissimilar groups and characteristics, so the business can target pricing, products, services, marketing messages and more.
"Multilayer perceptron (MLP) is a technique of feed
forward artificial neural network using back
propagation learning method to classify the target
variable used for supervised learning. It consists of multiple layers and non-linear activation allowing it to distinguish data that is not linearly separable."
QM0012- STATISTICAL PROCESS CONTROL AND PROCESS CAPABILITYsmumbahelp
This document provides information about a fully solved assignment for students. It lists the semester, specialization, subject code, name, credits, and marks. It also provides 6 questions related to statistical process control and process capability. For each question, it provides the evaluation scheme and space to write the answer. The questions cover topics like Pareto charts, scatter diagrams, Poisson distribution, hypothesis testing, analysis of variance, attribute control charts, and the methodology for statistical process control implementation.
This document provides information about assignment help available for various MBA subjects and semesters. Students can send their semester and specialization details to the provided email address or call the phone number to get fully solved assignments. The document then provides details of one such assignment on research methodology, including questions on topics like steps in research, exploratory research design, measurement scales, types of questionnaires, hypothesis testing, and structure of a research report. Guidelines are provided for writing effective research reports.
Validate data
Questionnaire checking
Edit acceptable questionnaires
Code the questionnaires
Keypunch the data
Clean the data set
Statistically adjust the data
Store the data set for analysis
Analyse data
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
Predictive Analytics for customer targeting identifies buying frequency, what causes customers to buy, factors informing purchases and messaging by segment.
This document contains summaries of answers to 6 questions about statistics concepts for a business management course. Question topics include: distinguishing between classification and tabulation; explaining arithmetic mean; calculating mean and standard deviation for combined data sets; expectations in a game of chance; probability of disease contraction among employees; applying a chi-squared test to determine if a population has decreased. Each answer is 1-2 paragraphs and refers the reader to an external site for full details.
This document contains sample questions and answers for a statistics assignment. It provides contact information to obtain fully solved assignments from professionals. The sample questions cover topics like the difference between classification and tabulation, calculating mean and standard deviation, probability questions, hypothesis testing, and an explanation of the chi-square test. Students can send their semester and specialization details to the provided email or call the given number to get their assignments completed by experts.
The document discusses several topics related to international marketing and management. It covers cultural imperatives, electives, and exclusives that influence business customs across cultures. It also discusses how American culture impacts management style, with an emphasis on individualism, objective decision making, and competition. Authority structures and decision making patterns are influenced by a country's power distance index. Management objectives around the world relate to security, personal life, affiliation, and power. Effective communication requires an understanding of differences in language and time orientation between low and high context cultures. Business ethics are also culturally dependent.
This document provides an overview of techniques for presenting numerical data in tables and charts. It discusses ordered arrays, stem-and-leaf displays, frequency distributions, histograms, polygons, ogives, bar charts, pie charts, and scatter diagrams. The chapter goals are to teach how to create and interpret these various data presentation methods using Microsoft Excel. Examples are provided for frequency distributions, histograms, polygons, and ogives to illustrate how to construct and make sense of these graphical representations of quantitative data.
This chapter discusses chi-square tests and nonparametric tests. It begins by introducing contingency tables and how they are used to classify sample observations according to multiple characteristics. Examples are provided to demonstrate how to set up contingency tables and calculate expected frequencies. The chapter then explains how to perform chi-square tests to analyze differences between two or more proportions, test independence between categorical variables, and compare population medians using the Wilcoxon rank-sum test. Decision rules for each test are outlined. Worked examples are provided to demonstrate applying these statistical tests and interpreting the results.
This document outlines an assignment for a research methodology course. It includes 5 questions assessing various aspects of the research process. Question 1 asks students to describe the typical steps carried out in a research study in 3 sentences or less. Question 2 asks students to explain the different types of descriptive research designs. Question 3 asks students to describe the steps involved in testing a hypothesis. Question 4 distinguishes between different research tools and explains the questionnaire design process. Question 5 defines analysis of variance, lists its assumptions, provides an example, and asks students to perform and interpret a two-way analysis of variance on a sample data set. Question 6 explains the structure of a research report and guidelines for effective report writing.
SMU MBA SPRING 2014 Solved ASSIGNMENT SEM-3 Common smumbahelp
This document provides information about obtaining fully solved SMU MBA Spring 2014 assignments. It lists contact information for a mail ID and phone number to request assignments. The document then lists sample questions from two MBA courses - Research Methodology and Legal Aspects of Business. The Research Methodology questions cover topics like research process, data collection methods, measurement scales, questionnaire design, and data analysis techniques. The Legal Aspects of Business questions discuss concepts like rights of a surety, duties of bailor and bailee, power of attorney, banking regulation, consumer protection act, and types of meetings. Students are advised to contact the provided mail ID or phone number to get the actual solved assignments.
This chapter discusses two-sample tests, including tests for the difference between two independent population means, the difference between two related (paired) sample means, the difference between two population proportions, and the difference between two variances. It provides the formulas and procedures for conducting Z tests, t tests, and F tests for these comparisons in situations where the population standard deviations are both known and unknown. The goal is to test hypotheses about differences between parameters of two populations or to construct confidence intervals for these differences.
This chapter discusses basic probability concepts, including defining probability, sample spaces, simple and joint events, and assessing probability through classical and subjective approaches. It also covers key probability rules like the general addition rule, computing conditional probabilities, statistical independence, and Bayes' theorem. The goals are to explain these fundamental probability topics, show how to apply common probability rules, and determine if events are statistically independent or dependent.
This chapter discusses fundamentals of hypothesis testing for one-sample tests. It covers:
1) Formulating the null and alternative hypotheses for tests involving a single population mean or proportion.
2) Using critical value and p-value approaches to test the null hypothesis, and defining Type I and Type II errors.
3) How to perform hypothesis tests for a single population mean when the population standard deviation is known or unknown.
This document provides an overview of key concepts in decision making covered in Chapter 16 of the textbook "Statistics for Managers Using Microsoft Excel". It begins by listing the chapter goals, which include describing decision making processes, constructing decision tables, applying expected value criteria, and accounting for risk attitudes. It then outlines the typical steps in decision making, such as listing alternatives and possible outcomes. Key decision making criteria are defined, like expected monetary value, expected opportunity loss, and value of perfect information. Examples are provided to demonstrate how to apply these concepts to make optimal decisions under uncertainty.
This chapter aims to teach students how to compute and interpret various numerical descriptive measures of data, including measures of central tendency (mean, median, mode), variation (range, variance, standard deviation), and shape (skewness). It covers how to find quartiles and construct box-and-whisker plots. The chapter also discusses population summary measures, rules for describing variation around the mean, and interpreting correlation coefficients.
This chapter discusses important discrete probability distributions used in statistics. It begins with an introduction to discrete random variables and probability distributions. It then covers the key concepts of mean, variance, standard deviation, and covariance for discrete distributions. The chapter focuses on explaining the binomial, hypergeometric, and Poisson distributions and how to calculate probabilities using them. It concludes with examples of how to apply these distributions to areas like finance.
The document discusses techniques for building multiple regression models, including:
- Using quadratic and transformed terms to model nonlinear relationships
- Detecting and addressing collinearity among independent variables
- Employing stepwise regression or best-subsets approaches to select significant variables and develop the best-fitting model
Data AnalysisResearch Report AssessmentBSBOllieShoresna
Data Analysis
Research Report Assessment
BSB123 Data Analysis
BSB123 Data Analysis
Notes on the Assessment
Covers Topics 1 – 10 i.e. descriptive statistics to Multiple Regression
Assignment is based around the international student recruitment industry looking specifically at students interested in postgraduate studies in USA
All 500 observations on spreadsheet are for international students
Variables are all related to factors which affect chance of being admitted and your job is to analyse this so that the company (GES) can advise future students about what to do and what their chances are of being admitted.
Report is split so that in each section you look at different aspects
You will need to do a summary incorporating elements of all of the parts to make recommendations.
Marks reflect (generally) the amount of work you need to do.
BSB123 Data Analysis
BSB123 Data Analysis
BSB123 Data Analysis
BSB123 Data Analysis
What am I looking for?
Can you select the correct technique / analysis to solve the question
Is that technique correctly and FULLY applied with calculations done correctly
E.g. in a hypothesis test, did you:
Correctly identify the test statistic (Z, T, F, χ2)
Did you include accurate hypotheses and decision rule which are consistent with each other
Were the calculations correct
Did you check to see if the assumptions or conditions of the test held
OR for Descriptive Statistics did you:
Consider all aspects of how you describe data and use the appropriate statistics to do that
Choose correct graph(s) for the type of data
Summarise the results to actually describe what you found – not just quote the stats.
Can you interpret the results – not just make a decision or complete a calculation.
Can you express the result in terms of the question and in a way which is understandable to your audience
In other words you will not get full marks unless you can correctly select the right approach to take for the data given, accurately and fully apply that analysis in a way which logically leads to a conclusion, make the conclusion in terms of the problem presented and then communicate that solution concisely and clearly
BSB123 Data Analysis
BSB123 Data Analysis
Examples from THA 4
H0: ≤ 700
H1: > 700
What is wrong with this?
BSB123 Data Analysis
BSB123 Data Analysis
Include title of analysis – t-Test: Two Sample Assuming Unequal Variances
5
Examples from THA 4
BSB123 Data Analysis
BSB123 Data Analysis
Look at t stat – all wrong – copied from somewhere – multiple students all getting it wrong
P and t test – do one
Used population terminology not sample
P-value – what is it?
6
Hypothesis Test
State the Hypotheses in terms of the parameter (µ,σ,p)
Identify the correct probability distribution (t, z, F, χ2)
Identify level of significance
State decision rule clearly
Use either test statistic method (i.e. in terms of t or z etc) or in terms of p-value. Don’t need to do both.
Decision rule must be consistent wit ...
This document provides information about obtaining fully solved assignments. It gives a mail ID and phone number to contact to receive assignments based on semester and specialization. It then provides a sample assignment on market research, including 6 questions and sample answers for questions 1, 2, 3, and 4. The assignment covers topics like research design, scales of measurement, hypothesis testing, and correlation.
This document provides information about getting fully solved assignments for various postgraduate programs and semesters. Students can send their semester and specialization details to the provided email ID or call the given phone number to get assignments. It includes details of subject codes, credits, and marks for assignments related to research methodology for programs like MBA, PGDM, PGDHRM etc. for semesters 1 and 3.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
This document provides information about an MBA course on research methodology. It includes 6 questions related to key concepts in research such as problem identification, data collection methods, sampling, data analysis, chi-square test, and analysis of variance. Contact information is provided at the top for students to get fully solved assignments or send their course details.
This document provides information about getting fully solved assignments from an assignment help service. It lists the contact email and phone number and specifies the programs and subjects they can provide assignments for, including research methodology, management subjects for various semesters, and more. It also provides an example of a research methodology assignment question that is answered in detail.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
This document provides information about obtaining fully solved assignments from an assignment help service. It lists contact details including a phone number and email address to call or send details of semester and specialization to get help with MBA assignments. It then provides details of an MBA statistics assignment for semester 1 on the topic of statistics for management, including 6 questions to answer with evaluation criteria for each.
This document provides information about getting fully solved assignments from an assignment help service. It lists the contact email and phone number and provides details about the available programs, subjects, credits, and marks. It also includes notes about answering all questions and word counts for longer questions. The document then provides 6 sample questions related to research methodology, with multiple parts to each question.
This document provides information about getting fully solved assignments from an assignment help service. It lists the contact email and phone number and provides details about the available programs, semesters, subjects, and questions included in the assignments. It discusses research processes, data collection methods, measurement scales, reliability and validity concepts, questionnaire administration modes, analysis of variance techniques, and principles of professional ethics in research.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
As mentioned earlier, the mid-term will have conceptual and quanti.docxfredharris32
As mentioned earlier, the mid-term will have conceptual and quantitative multiple-choice questions. You need to read all 4 chapters and you need to be able to solve problems in all 4 chapters in order to do well in this test.
The following are for review and learning purposes only. I am not indicating that identical or similar problems will be in the test. As I have indicated in the class syllabus, all the exams in this course will have multiple-choice questions and problems.
Suggestion: treat this review set as you would an actual test. Sit down with your one page of notes and your calculator, and give it a try. That way you will know what areas you still need to study.
ADMN 210
Answers to Review for Midterm #1
1) Classify each of the following as nominal, ordinal, interval, or ratio data.
a. The time required to produce each tire on an assembly line – ratio since it is numeric with a valid 0 point meaning “lack of”
b. The number of quarts of milk a family drinks in a month - ratio since it is numeric with a valid 0 point meaning “lack of”
c. The ranking of four machines in your plant after they have been designated as excellent, good, satisfactory, and poor – ordinal since it is ranking data only
d. The telephone area code of clients in the United States – nominal since it is a label
e. The age of each of your employees - ratio since it is numeric with a valid 0 point meaning “lack of”
f. The dollar sales at the local pizza house each month - ratio since it is numeric with a valid 0 point meaning “lack of”
g. An employee’s identification number – nominal since it is a label
h. The response time of an emergency unit - ratio since it is numeric with a valid 0 point meaning “lack of”
2) True or False: The highest level of data measurement is the ratio-level measurement.
True (you can do the most powerful analysis with this kind of data)
3) True or False: Interval- and ratio-level data are also referred to as categorical data.
False (Interval and ratio level data are numeric and therefore quantitative, NOT qualitative….Nominal is qualitative)
4) A small portion or a subset of the population on which data is collected for conducting statistical analysis is called __________.
A sample! A population is the total group, a census IS the population, and a data set can be either a sample or a population.
5) One of the advantages for taking a sample instead of conducting a census is this:
a sample is more accurate than census
a sample is difficult to take
a sample cannot be trusted
a sample can save money when data collection process is destructive
6) Selection of the winning numbers is a lottery is an example of __________.
convenience sampling
random sampling
nonrandom sampling
regulatory sampling
7) A type of random sampling in which the population is divided into non-overlapping subpopulations is called __________.
stratified random sampling
cluster sampling
systematic random sampling
regulatory sampling
8) A ...
This document provides information about an MBA research methodology assignment that can be purchased for Rs. 125 per solved question. It includes 6 questions related to defining business research, descriptive research designs, measurement scales, sampling methods, coding questions, and the structure of a research report. Students are to answer each question in 300-400 words. Contact information is provided to purchase the solved assignments.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
SMU ASSIGNMENTS, SMU MBA ASSIGNMENTS, SMU MBA SUMMER SOLVED ASSIGNMENTS, SMU MBA FALL SOLVED ASSIGNMENT, SMU MBA SUMMER ASSIGNMENTS SEM 1, SMU MBA SEM 1 ASSIGNMENTS, SMU MBA SEM 1 SOLVED ASSIGNMENTS, SMU MBA SEM 2 SOLVED ASSIGNMENTS, SMU MBA SEM 3 SOLVED ASSIGNMENTS, SMU MBA SEM 4 SOLVED ASSIGNMENTS, SMU MBA SEM 2 ASSIGNMENTS, SMU MBA SEM 3 ASSIGNMENTS, SMU MBA SEM 4 ASSIGNMENTS, SMU MBA MODEL PAPERS
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
This document provides information about obtaining fully solved assignments for SMU BBA Spring 2014 semester. It lists 6 assignments with multiple questions covering topics like statistical surveys, probability, hypothesis testing, time series analysis, cost of living index, analysis of variance, and comparing population means. Contact details are provided to send semester and specialization details to get the solved assignments via email or phone.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
The document provides information about getting fully solved assignments for various MBA programs by emailing or calling with semester and specialization details. It then provides a sample assignment question paper on project management in retail with questions on topics like sensitivity analysis, detailed project reports, network planning techniques, conflict management in projects, organizational planning, and short notes on Gantt charts and fishbone diagrams. Students are advised to answer all questions, with approximately 400 word answers for 10 mark questions.
The document provides information about getting fully solved assignments for various MBA programs by emailing or calling with semester and specialization details. It then provides a sample assignment question paper on project management in retail with questions on topics like sensitivity analysis, detailed project reports, network planning techniques, conflict management in projects, organizational planning, and short notes on Gantt charts and fishbone diagrams. Students are advised to answer all questions, with approximately 400 word answers for 10 mark questions.
1. Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
ASSIGNMENT
DRIVE SUMMER 2014
PROGRAM MBADS / MBAHCSN3 / MBAN2 / PGDBAN2 / MBAFLEX
SEMESTER I
SUBJECT CODE & NAME MB0040 - STATISTICS FOR MANAGEMENT
BK ID B1731
CREDITS 4
MARKS 60
Note: Answer all questions. Kindly note that answers for 10 marks questions should be
approximately of 400 words. Each question is followed by evaluation scheme.
1 Distinguish between Classification and Tabulation. Explain the structure and components of a
Table with an example.
Answer : Classification vs Tabulation
Both classification and tabulation are methods of summarizing data in statistics, which makes further
analysis of data to draw inferences from the data. In this article, we will discuss in detail the two
methods of summarizing the data and distinguish between classification and tabulation of data.
What is Classification of Data?
In statistics, classification is the process of separation of data into several classes or groups using
properties in the data set. For example, the
2 a) Describe the characteristics of Normal probability distribution.
Answer : The Normal Probability Distribution is very common in the field of statistics.
Whenever you measure things like people's height, weight, salary, opinions or votes, the graph of
the results is very often a normal curve.
The Normal Distribution
A random variable X whose distribution has the shape of a normal curve is called a normal random
variable.
2. b) In a sample of 120 workers in a factory, the mean and standard deviation of wages were Rs.
11.35 and Rs.3.03 respectively. Find the percentage of workers getting wages between Rs.9 and
Rs.17 in the whole factory assuming that the wages are normally distributed.
Answer :
3 a) The procedure of testing hypothesis requires a researcher to adopt several steps.
Describe in brief all such steps.
Answer : Five Steps of Hypothesis Testing
The basic logic of hypothesis testing is to prove or disprove the research question. By only allowing
an error of 5% or 1% and making correct decisions based on statistical principles, the researcher can
conclude that the result must be real if chance alone could produce the same result only 5% of the
time or less. These five steps consists of
b) Distinguish between:
i. Stratified random sampling and Systematic sampling
Answer : Stratified Random Sampling
Stratified Random Sampling, also sometimes called proportional or quota random sampling, involves
dividing your population into homogeneous subgroups and then taking a simple random sample in
each subgroup. In more formal terms:
Objective: Divide the population into non-overlapping groups (i.e., strata) N1, N2, N3, ... Ni, such
that N1 + N2 + N3 + ... + Ni = N. Then do a
ii. Judgement sampling and Convenience sampling
Answer : Judgmental sampling is a non-probability sampling technique where the researcher selects
units to be sampled based on their knowledge and professional judgment.
Purposive sampling is used in cases where the specialty of an authority can select a more
representative sample that can bring more
4 a) What is regression analysis? How does it differ from correlation analysis?
Answer : Regression analysis:
In statistics, regression analysis is a statistical process for estimating the relationships among
variables. It includes many techniques for modeling and analyzing several variables, when the focus
is on the relationship between a
b) Calculate Karl Pearson’s coefficient of correlation between X series and Y series.
3. x 110 120 130 120 140 135 155 160 165 155
y 12 18 20 15 25 30 35 20 25 10
Answer :
5 Briefly explain the methods and theories of Business forecasting.
Answer : Business forecasting provides a guide to long-term strategic planning and helps to inform
decisions about scheduling of production, personnel and distribution. These are common statistical
tasks in business that are often done poorly and frequently confused with planning and setting of
goals. The Programme in Business Forecasting introduces participants to forecasting techniques and
provides a practical understanding of the main forecasting tools used by economists, as well as
business, marketing and financial analysts.
Business Forecasting Methods
Q6 Construct Fisher’s Ideal Index for the given information and check whether Fisher’s formula
satisfies Time Reversal and Factor Reversal Tests.
Items P0 Q0 P1 Q1
A 16 5 20 6
B 12 10 18 12
C 14 8 16 10
D 20 6 22 10
E 80 3 90 5
F 40 2 50 5
Answer :
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )