Management Notes

# Management Notes

Reference Notes for Management

## Time Series Vs Cross Sectional Data – Major Differences Explained | Business Statistics

A time series and a cross-sectional data set are two types of data commonly used in statistical analysis. They are collected and organized differently, and they are analyzed and interpreted differently.

## Time Series Data:

A time series of data is a series of observations made at different points in time over a sequence of equally spaced time intervals. It involves observing a variable or multiple variables over a sequence of equally spaced time intervals.

There are many fields in which time series data can be used for analyzing trends, patterns, and forecasting future values, including finance, economics, meteorology, and social sciences.

## Difference between Parametric and Non Parametric Statistics

### Parametric Statistics

Parametric statistics is a branch of statistics which assumes that the data have come from a type of probability distribution and makes inferences about the parameters of the distribution. Most well known elementary statistical methods are parametric. The difference between parametric model and non-parametric model is that the former has a fixed number of parameters, while the latter grows the number of parameters with the amount of training data.

## Which of the following is true regarding Data Acquisition

A) Because data acquisition is often technical, the research team does not need to be involved and it can be outsourced to external professionals.
B) A data acquisition plan is not needed because the process can be very flexible to accommodate changes that occur as the research unfolds.
C) Data acquisition should follow a detailed collection plan that is set in advance.
D) Existing data sets from other researchers can be used without restriction.

## Target Population Definition

In this study, the main purpose is to describe the complete collection of objects. The selection of the target population is the first step of an observational or experimental study and is often a difficult one. The term “target population” and the term “population” are most often synonymous. Even if we sample correctly, sometimes we don’t hit the target: samples may not be representative of the population that we originally intended to sample.

## Kurtosis

➦ Even if we know about the measures of central tendency, dispersion, and skewness, we cannot fully comprehend a distribution.

➦ For a complete understanding of the shape of the distribution, we should also know another measure called Kurtosis.

➦ It is called the “convexity of a curve” by Prof. Karl Pearson. It measures the flatness of distributions.

## Lorenz Curve

### Concept:

➦ Max Lorenz developed the Lorenz curve in 1905 as a graphical representation of income inequality and wealth inequality.

➦ This graph shows population percentiles according to income or wealth on a horizontal axis.

➦ It plots cumulative income or wealth on the vertical axis, so that an x-value of 45 and a y-value of 14.2 indicates that the bottom 45% of the population controls 14.2% of the total income or wealth.

➦ A Lorenz curve is usually determined from incomplete income or wealth observations. It is generally used to show the extent of concentration of income and wealth.

## Importance of Statistics in Business

Statistics focuses on the study and manipulation of data, as well as gathering, documenting, reviewing, analyzing, and drawing conclusions from it.
The term statistics refers to the entire body of tools that are used to collect data, organize and interpret them, and, finally, draw conclusions from them.
For quantitative data to be useful, both statistical aspects need to be considered. We cannot know the right procedure to extract from data the information they contain if statistics, as a subject, is inadequate and has poor methodology.
Even if our data are deficient or inaccurate, we cannot reach the right conclusions if they are inadequate or insufficient.
In any business enterprise, statistical methods can be used for three major purposes. Among them are:

## Types of Data | Qualitative Data and Quantitative Data | Business Statistics

### Types of Data

Statistics is based on statistical data. Depending on what we are interested in, the data may pertain to a phenomenon, a situation under study, or an activity that is of interest to us. A measurement, a count or an observation is what they represent. Therefore, statistical data refers to those aspects of a problem situation that can be measured, quantified, counted, or classified. A variable is the result of the interaction of an object with data, or the activity of generating it. Variables are also those that show a degree of variability among consecutive measurements.

Statistics classifies data into two broad categories: Quantitative and Qualitative. It identifies characteristics that can be measured and categorizes them accordingly. There are definite units of measurement that can be used to quantify quantitative data. The units of measurement are characteristics whose successive measurements yield quantifiable observations.

## Explain what each point on the least-squares regression line represents.

Explain what each point on the least-squares regression line represents. Options A) Each point on the​ least-squares regression line represents the​ y-value of the data set at that corresponding value of x. B) Each point on the​ least-squares regression line represents the predicted​ y-value at the corresponding value of x. C) Each point on the​ … Read more

## Which of the following statements about correlation is true?

A) we say there is a positive correlation between x and y if there is no distinct pattern in the scatterplot.
B) we say that there is a positive correlation between x and y if the x-values increase as the corresponding y-values increase.
C) we say that there is a positive correlation between x and y if the x-vales increase as the corresponding y-values decrease.
D) we say that there is a negative correlation between x and y if the x-values increase as the corresponding y-values increase.

## Which of the following is not a measure of dispersion?

A)Variance
B) Range
C) Arithmetic Mean
D) Standard Deviation

The Correct Answer for the given question  is Option C) Arithmetic Mean

Arithmetic Mean is not a measure of dispersion. Arithmetic mean is not a measure of dispersion because it does not take into account the variability in the data. Scores on a test can be skewed by one or more students who perform extraordinarily well, while scores for other students may be quite low. A better measure of variation would be the standard deviation, which takes into account both high and low scores. Dispersion is best measured by the standard deviation.

Arithmetic mean (or average) is a number that summarizes the central tendency of a set of data. It is calculated by adding up all the values in a dataset and dividing that total by the number of items in the dataset. The arithmetic mean can be used to compare groups of data, to find patterns, or to make comparisons between two datasets.