<p style="text-align:justify"><span style="font-size:14px"><span style="color:black">The science of </span><strong><span style="color:black">statistics </span></strong><span style="color:black">deals with the collection, analysis, interpretation, and presentation of </span><strong><span style="color:black">data</span></strong><span style="color:black">. We see and use data in our everyday lives.</span></span></p>

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<p style="text-align:justify"><span style="font-size:14px">In this course, you will learn how to organize and summarize data. Organizing and summarizing data is called <strong>descriptive statistics</strong>. Two ways to summarize data are by graphing and by using numbers, for example, finding an average. After you have studied probability and probability distributions, you will use formal methods for drawing conclusions from <em>good </em>data.</span></p>

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<p style="text-align:justify"><span style="font-size:14px">The formal methods are called <strong>inferential statistics</strong>. Statistical inference uses probability to determine how confident we can be that our conclusions are correct.</span></p>

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<p style="text-align:justify"><span style="font-size:14px">Effective interpretation of data, or inference, is based on good procedures for producing data and thoughtful examination of the data. You will encounter what will seem to be too many mathematical formulas for interpreting data. The goal of statistics is not to perform numerous calculations using the formulas, but to gain an understanding of your data. The calculations can be done using a calculator or a computer. The understanding must come from you. If you can thoroughly grasp the basics of statistics, you can be more confident in the decisions you make in life.</span></p>

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<p style="text-align:justify"><span style="font-size:14px"><strong>Statistical Models</strong></span></p>

<p style="text-align:justify"><span style="font-size:14px"><span style="color:black">Statistics, like all other branches of mathematics, uses </span><strong><span style="color:black">mathematical models </span></strong><span style="color:black">to describe phenomena that occur in the real world. Some mathematical models are deterministic. These models can be used when one value is precisely determined from another value. Examples of deterministic models are the quadratic equations that describe the acceleration of a car from rest or the differential equations that describe the transfer of heat from a stove to a pot. These models are quite accurate and can be used to answer questions and make predictions with a high degree of precision. Space agencies, for example, use deterministic models to predict the exact amount of thrust that a rocket needs to break away from Earth&rsquo;s gravity and achieve orbit.</span></span></p>

<p style="text-align:justify"><span style="font-size:14px"><span style="color:black">However, life is not always precise. While scientists can predict to the minute the time that the sun will rise, they cannot say precisely where a hurricane will make landfall. </span><strong><span style="color:black">Statistical models </span></strong><span style="color:black">can be used to predict life&rsquo;s more uncertain situations. These special forms of mathematical models or functions are based on the idea that one value affects another value. Some statistical models are mathematical functions that are more precise&mdash;one set of values can predict or determine another set of values. Or some statistical models are mathematical functions in which a set of values do not precisely determine other values. Statistical models are very useful because they can describe the probability or likelihood of an event occurring and&nbsp;&nbsp;&nbsp; provide alternative outcomes if the event&nbsp;does not occur. For example, weather forecasts are examples of statistical models. Meteorologists cannot predict tomorrow&rsquo;s weather with certainty. However, they often use statistical models to tell you how likely it is to rain at any given time, and you can prepare yourself based on this probability.</span></span></p>