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# Linear Regression Summary table in SPSS

In this section, we will learn about the remaining table of **Linear regression**. We will learn about the **ANOVA table** and the **Coefficient table**. Both the tables are given below:

First, we will learn about the ANOVA summary table. While we calculate the linear regression, we will get an ANOVA summary table because ANOVA is essentially a precursor to **cause** and **effect** analysis or linear modeling. In case, we are looking for a cause and effect analysis, and if we divide the influence of independent variable into **many categories** or many levels like a **lower** level of Iv (Independent variable), **medium** level of Iv and **high** level of Iv, and if these three levels of Iv have a **significant influence** on the dependent variable, then itâ€™s worthwhile to look for an actual regression equation. So, in this case, **different levels** of Independent variables are being compared, and we have found that this influence is significant in the **ANOVA table**. Keep this in mind that for the **linear regression** equation to be **valid**, this ANOVA should be **significant**, and **R square** should be **sufficiently high**. So our **ANOVA** is **significant**. It means we are good to go for the **linear regression** analysis, and that is our **last table** for the outcome.

The **last table** gives us a **Constant value**, and then we have the value of the **unstandardized coefficients** that are the **B** and with its **standard error**. After that, we have the **standardized coefficient** value that is the **Beta**.

The variables we have are **Constant** and **Advertising spending**. So **B** and **Beta** are slightly different in terms of the kind of **units** that are used to report them. **B** is **unit free. For example**, if we spend on the **advertisement**, it might be in terms of **dollars** or our **local currency**. So if we report this effect, we will say that **independent variables** are measured in **local units**. One unit of the **independent variable**, measured in the **local unit**, has a **1.073 unit** of **positive** influence on the **dependent** variable.

It means **advertisement** has a **positive** influence on the **dependent** variable, and we are just indicating the result in the local currency. **For example, advertisement spending** has not been **reported**, like in which **currency** the spending has been measured.

So maybe we are having **thousands** of **dollars** or something we are not aware of it. Even in this case, when we are not aware of the **currency** and **unit**, we can say that **1 unit** spending in advertisement leads to **1.073 increases** in **sales**. So we are getting an almost equal amount of increase in **sales** for the **advertisement spending** in the case of **B** and **Beta**.

**Beta** is free from any **unit**. So **beta** is measured or reported in terms of **standard deviation**. In this case, we will say that **one standard deviation** change in the advertisement spending will cause a **.916** standard deviation change in sales.

So again, this influence is **positive**. The difference between **B** and **Beta** is that **Beta** is **neutral**, and it is not any **local unit** or **currency**, and **B** is always in terms of a local unit or the currency. So, the standard way of reporting the **linear regression** outcome is **Beta**. We generally donâ€™t report the **B** unless or until we are creating the **table** as well. In the **tables**, we can report **B** as well as the **beta**. But in the case of **statements**, we report only the standard **beta coefficient**.

Then we have the **t statistics** here. **T-test** measures that whatever influences we have got, whether they are significantly different from **zero**. So thatâ€™s very important. So we might be having a **non-significant** difference as compared to a **null number**. So that influence is accounted by using the **t stat**, and **t stat** is again **highly significant**.

So we can say, all in all, **advertisement spending** has a **positive** influence on **sales**. In fact, one **standard deviation** change in **advertisement** spending leads to a **.916** standard deviation change in the sale.