|1. |If a variable has possible values –2, 6, and 17, then this variable is | |A) |both a continuous and a discrete variable | |B) |a continuous variable | |C) |neither a continuous nor a discrete variable | |D) |a discrete variable |

|2. |Based on the following graph, what conclusion could you make comparing how well students did on their statistics exam as a | | |function of how many hours they spent preparing for the exam? | | |[pic] | |A) |Hours spent preparing for an exam is a dependent variable. | |B) |There is no relationship between the number of hours spent preparing for the exam and the average grade. | |C) |There is a possible relationship between grades and time spent preparing for the exam. | |D) |Grades are independent of preparation time. |

|3. |If a researcher manipulates one of the variables and tries to determine how the manipulation influences other variables, the | | |researcher is conducting a(n) | |A) |manipulative study....

...Chapter 1: Introduction to Statistical Data
April-10-13 7:53 AM
1.1 Why Study Statistics? Definitions:
1) Statistics: defined as the art of collecting, classifying, interpreting, and reporting numerical information related to a particular subject 2) Population: The total set of objects or measurements that are of interest to a decision maker 3) Descriptive Statistics: focused on summarizing and presenting information. - Examples: Pie chart or graph comparing populations of different provinces, table comparing the amounts of ozone over North America over a series of years or a world map showing imports of Brazilian cherry wood by various countries
4) Data set: the set of all observations for a given project or purpose - There is some choice in how to present these data - whether using tables, different types of graphs, or other illustrations - The best choice is one that conveys the necessary information as clearly as possible, while reflecting the level of knowledge of the audience - Keep it simple! 5) Inferential Statistics: its techniques are designed for making estimates, or inferences, about the characteristics of a population based on information found in one or more samples 6) Samples: representative subsets of the population Two Main types of problems call for inferential statistics: 1. Estimate a characteristic of a population, based on data from a sample Example: A survey finds that 52%...

...Omkar & Yaying
Wednesday 5-6pm
WEEK 3 BES PASS
Descriptive Statistics Population - a set of all possible observations. Sample - a portion of a population. We often use information concerning a sample to
make an inference (conclusion) about the population.
Parameter - describes a characteristic of the population, eg: the population variance Statistic- describes a characteristic of a sample, eg: the sample variance
Frequency Distribution and Histograms Class - a collection of data which are mutually exclusive Frequency distribution - a grouping of data into classes Relative frequency distribution - calculates the number of data in a class as a percentage
of the total data
Shapes of Distributions and Histograms
A histogram is symmetrical if one half of the histogram is a mirror reflection of the other Non-symmetrical distributions are said to be “skewed”
a) Skewed to the right (Positively skewed) Mode < Median < Mean
b) Skewed to the left (Negatively skewed) Mode > Median > Mean
c) Symmetric Distribution Mode = Median = Mean
Measures of Central Tendency: The Mean, Mode and Median The mean is the average of scores: Population mean: μ = Σ xi/N
Sample mean: x = Σ xi/n
The mode is the value that has the highest frequency The median is the middle value of data ordered from lowest to highest The median and the mode are relatively less sensitive to outliers.
Quartiles and Percentiles,...

...Statistics in Business
Christian A. Coronel
Qnt/351
June 10, 2013
Jeffrey Greene
Statistics in BusinessStatistics is the science of collecting, organizing, analyzing, interpreting, and presenting data. Some experts prefer to call statistics data science, a trilogy of tasks involving data modeling, analysis, and decision making. In contrast, a statistic is a single measure, reported as a number, used to summarize a sample data set. Knowing statistics will make you a better consumer of other people’s data. You should know enough to handle everyday data problems, to feel conﬁdent that others cannot deceive you with spurious arguments, and to know when you’ve reached the limits of your expertise. Statistical knowledge gives your company a competitive advantage against organizations that cannot understand their internal or external market data. And mastery of basic statistics gives you, the individual manager, a competitive advantage as you work your way through the promotion process, or when you move to a new employer.
Nominal Level of Measurement
The nominal level of measurement is the lowest of the four ways to characterize data. Nominal means "in name only" and that should help to remember what this level is all about. Nominal data deals with names, categories, or labels. Data at the nominal level is qualitative. Colors of eyes, yes or no...

...manipulated or processed
into information that is valuable to decision making. The processing and manipulation of raw
data into meaningful information are the heart of data analysis. Data analysis includes data
description, data inference, the search for relationships in data and dealing with uncertainty
which in turn includes measuring uncertainty and modelling uncertainty explicitly.
In addition to data analysis, other decision making techniques are discussed. These techniques
include decision analysis, project scheduling and network models.
Chapter 1 illustrates a number of ways to summarise the information in data sets, also known as
descriptive statistics. It includes graphical and tabular summaries, as well as summary measures
such as means, medians and standard deviations.
Uncertainty is a key aspect of most business problems. To deal with uncertainty, we need a basic
understanding of probability. Chapter 2 covers basic rules of probability and in Chapter 3 we
discuss the important concept of probability distributions in some generality.
In Chapter 4 we discuss statistical inference (estimation), where the basic problem is to estimate
one or more characteristics of a population. Since it is too expensive to obtain the population
information, we instead select a sample from the population and then use the information in the
sample to infer the characteristics of the population.
In Chapter 5 we look at the topic of...

...
Business Analytics: Unit 1: Descriptive Statistics and Mathematical Foundations
Kaplan University
March 23, 2014
Descriptive Statistics and Mathematical Foundations
Part I: Pie Chart & Bar Graph
This information regards T-100 Domestic Market’s boarding information during the previous year for the top seven airlines in the United Sates. According to the data Southwest Airlines boarded 81.1 million; Delta Airlines, 79.4 million; American Airlines, 72.6 million; United Airlines, 56.3 million; Northwest Airlines, 43.3 million; U.S. Airways, 37.8 million, and Continental Airlines, 31.5 million (KU, 2014).
This is ungrouped data that needs to be grouped into a pie chart and a bar graph. The bar graph and pie chart both lists nonmetric (qualitative) descriptive statistics. The descriptive statistics are called, ordinal statistics which rank each airline from highest to lowest or lowest to highest annual boarding information (Black, 2012). The pie chart and bar graph summarizes the top seven airlines previous years boarding data. First, I will discuss the pie chart. The pie chart below shows the percentage breakdown of each airline’s annual boarding information. Each of the breakdowns represents the magnitude of the whole pie chart in percentages (Black, 2012). As you will notice that the leaders in the airline industry is Southwest and Delta Airlines with 20 percent...

...
Statistics in Business
Katherine Wolf
QNT/275
April 3, 2015
Barry Adkins
Statistics in BusinessStatistics is all about collecting numerical data, organizing it, and interpreting the data to form hypothesis. While interpreting the data we are able to draw all kinds of conclusions from weather forecasts to how much money a company is projected to make over the next year. There are different types ofstatistics that can be used for different outcomes. You have to first identify the objective and the type of data that you will be interpreting.
Types and Levels of Statistics
There are two main type of statistics; Quantitative and Qualitative. Quantitative is data that can be measured and written down in numbers. Qualitative is data that can’t truly be measured. For example, the number of cars Jones Junction sold in 2014 would be Quantitative data. If Jones Junction looked presentable or not is an example of Qualitative data. The four different levels of measurement are nominal, ordinal, interval, and ratio. The different levels of measurement are used to determine what type of statistical data you are analyzing.
Role in Business Decision-Making
Statistics plays a major role in the decision–making for businesses. Statistics are able to analyze data on employee performance. For example, if there...

...
Statistics in Business
QNT/351
Donald Lifke
Statistics in BusinessStatistics is defined as the science of data. It involves collecting, classifying, summarizing, organizing, analyzing, and interpreting numerical information. (McClave, Benson, & Sincich, 2011, p. 3). . There are two different application process involved in statistics; descriptive and inferential. Descriptivestatistics is the analysis that helps describe, summarize or show data in a way to allow patterns to emerge from the data. This can be captured numerically or graphically. Inferential statistics require the statistician to reach conclusions in way that extends beyond the immediate data alone, to draw an inference of the data.
Our text states there are two general types of statistics; qualitative and quantitative. Qualitative data are measurements that cannot be measured on a natural numerical scale; they can only be classified into one group of categories. It is also sub classified as interval or ratio. Where quantitative data is the opposite, it only recorded on a naturally occurring numerical scale. It is sub classified nominal or ordinal. (McClave, Benson, & Sincich, 2011, p. 13).
Statistics play a major role in business decision making on a daily basis. Statistics provide businesses with...

...Statistics in Business
QNT/351
William Modey
Quantitative Analysis for Business
Salonyia Fisher
Summary
Statistics is accurately defined as the study of the analysis, data collection, and organization of the data which is interpreted by a particular business field. Statistics main focus is usually dealing with the preparation procedure of the data collection in the course of developing surveys and creating experiments. When an organization uses statistics, it needs to be taken into consideration that there are two main types of statistics, which are inferential statistics and descriptive statistics. Descriptive statistics targets to sum up data sets, instead of just using data collected from a given population. Inferential statistics is used to describe different systems of procedures that can draw conclusions from arising datasets pretentious by random variations.
Methods of Statistics
There are several different methods of statistics. The first method is experimental method, which uses several different steps in this process that includes planning the research process, designing the experiments, performing the experiments, further examining the datasets, and documenting the results of the study. The second method is the observational study, which explores the...