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   Andrei Denice N. Baclay Mr. German Manzanero BSBA- I October 12, 2018 Assignment in Business Statistics. Research the following: 1.   Division of Statistic Descriptive statistics  -Descriptive statistics are brief descriptive coefficients that summarize a given data set, which can be either a representation of the entire or a sample of a population. Example . At least 5% of all fires reported las year in Lahore were deliberately set. Inferential statistics -Inferential statistics is one of the two main branches of    statistics. It is use a random sample of data taken from a population to describe and make inferences about the population. Example . As a result of recent poll, most Pakistans are in favor of independent and powerful parliament. 2.   Types of data Numerical, Categorical, and Ordinal Numerical data - These data have meaning as a measurement, such as a person’s height, weight, IQ, or blood pressure; or they’re a count, such as the number of stock shares a person owns, how many teeth a dog has, or how many pages you can read of your favorite book before you fall asleep.    Discrete data  represent items that can be counted; they take on possible values that can be listed out. The list of possible values may be fixed (also called finite); or it may go from 0, 1, 2, on to infinity (making it countably infinite).    Continuous data  represent measurements; their possible values cannot be counted and can only be described using intervals on the real number line. Categorical data - Categorical data represent characteristics such as a person’s gender, marital status, hometown, or the types of movies they like. Categorical data can take on numerical values (such as “1” indicating male and “2” indicating female), but those numbers don’t have mathematica l meaning. Ordinal data  - mixes numerical and categorical data. The data fall into categories, but the numbers placed on the categories have meaning.  3.   Types of Variables and Classification of Variables Categorical variable : variables than can be put into categories. For example, the category “Toothpaste Brands” might contain the variables Colgate and Aqua  fresh. Confounding variable : extra variables that have a hidden effect on your experimental results. Continuous variable : a variable with infinite number of values, like “time” or “weight”.   Control variable : a factor in an experiment which must be held constant. For example, in an experiment to determine whether light makes plants grow faster, you would have to control for soil quality and water. Dependent variable : the outcome of an experiment. As you change the independent variable, you watch what happens to the dependent variable. Discrete variable : a variable that can only take on a certain number of values. For example, “number of cars in a parking lot” is discrete because a car park can only hold so many cars. Independent variable : a variable that is not affected by anything that you, the researcher, does. Usually plotted on the x-axis. Nominal variable : another name for categorical variable. Ordinal variable:  similar to a categorical variable, but there is a clear order. For example, income levels of low, middle, and high could be considered ordinal. Qualitative variable:   a broad category for any variable that can’t be counted (i.e. has no numerical value). Nominal and ordinal variables fall under this umbrella term. Quantitative variable:  A broad category that includes any variable that can be counted, or has a numerical value associated with it. Examples of variables that fall into this category include discrete variables and ratio variables. Random variables  are associated with random processes and give numbers to outcomes of random events. A  ranked variable  is an ordinal variable; a variable where every data point can be put in order (1st, 2nd, 3rd, etc.). Ratio variables:  similar to interval variables, but has a meaningful zero.
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