Toward the end of the twentieth century, the values of stocks of internet and technology companies rose dramatically. As a result, the Standard and Poor’s stock market average rose as well. The Standard and Poor’s Index tracks the value of that initial investment of just under $100 over the 40 years. It shows that an investment that was worth less than $500 until about 1995 skyrocketed up to about $1100 by the beginning of 2000. That five-year period became known as the “dot-com bubble” because so many internet startups were formed. As bubbles tend to do, though, the dot-com bubble eventually burst. Many companies grew too fast and then suddenly went out of business. The result caused the sharp decline represented on the graph beginning at the end of 2000.
Notice, as we consider this example, that there is a definite relationship between the year and stock market average. For any year we choose, we can determine the corresponding value of the stock market average. In this chapter, we will explore these kinds of relationships and their properties.
In this chapter, you will learn to:
- Solve linear equations
- Solve linear inequalities, expressing solutions on a number line and in interval notation
- Graph linear equations
- Find the equation of a line
- Apply linear models to data
- 1.1: Solving Linear Equations and Inequalities
- An equation is a statement indicating that two algebraic expressions are equal. A linear equation with one variable, x , is an equation that can be written in the standard form ax+b=0 where a and b are real numbers and a≠0 . A solution to a linear equation is any value that can replace the variable to produce a true statement.
- 1.2: Graphing Linear Equations
- Equations whose graphs are straight lines are called linear equations. A line is completely determined by two points. Therefore, to graph a linear equation we need to find the coordinates of two points. This can be accomplished by choosing an arbitrary value for x or y and then solving for the other variable.
- 1.5: Fitting Linear Models to Data
- Scatter plots show the relationship between two sets of data. Scatter plots may represent linear or non-linear models. The line of best fit may be estimated or calculated, using a calculator or statistical software. Interpolation can be used to predict values inside the domain and range of the data, whereas extrapolation can be used to predict values outside the domain and range of the data. The correlation coefficient, r , indicates the degree of linear relationship between data.