## Excel – Linear Estimations for Better Decision Making

Excel is a brilliant tool for data analysis.

A useful function or two can help, as several factors can impact your data. Weather, conflicts around the world, inflation, the interest rate, currency rates, subcontractors, number of customers, competitors, special offers, market size etc.

## Trend Function and Regression Tool

In this blog post, we’ll be looking at the TREND function and the REGRESSION tool from the ANALYSIS TOOLPAK and how you can use them.

In this example, expected key influencers on sales numbers are marketing expenses, how many salespeople, number of sales calls, and the dollar value. Many clients are in the USA.

At this point, it is  a clever idea to analyse if these key influencers are the right variables to get accurate estimates. You can do this with the REGRESSION tool from the Excel ANALYSIS TOOLPAK.

In the Regression dialog box, enter the range of your y-values (dependent variable) in the Input Y Range field. Enter the range of your x-values (independent variable) in the Input X Range field. Check the “Labels” box if your data has column labels.

Choose an output location for the results of the regression analysis. Check any additional output options you want to include, such as confidence intervals or residuals. Click OK to run the regression analysis. The results, including the slope, intercept, R-squared, and other statistics, will be displayed in the output location you chose.

## The F-Test

The F-Test of overall significance in regression is a test of whether your linear regression model provides a better fit to a dataset than a model with no predictor variables. Linear regression needs the relationship between the independent and dependent variables to be linear. The significance “F” gives you is the probability that the model is wrong.

Highlighted in the below screen grab. We want the significance F or the probability of being wrong to be as small as possible. Smaller is better. Many mathematicians agree that a significance F > 0.005 indicates that the model is wrong. This would mean that the data set cannot be treated as linear.

The significance F in the output report is almost nothing. It indicates that the linear approach is correct. Next step is to identify if all the independent variables are significant for an accurate estimation. The P-Value will tell you.

## The P Value

A p-value < 0.05 is good. A p-value< 0.05 means there is a 5% probability that there is no relationship between the variables.

The p-value for the independent variable GBP to USD is 36% which means that there is a 36% probability that there is no relationship between the variables, and for the accuracy of the predictions it should not be taken to account.

The other 3 independent variables return a low p-value and are closely related to the sales and will be important for the estimation of the sales.

The report also shows the slopes.

 Marketing Expenses 7.7532 Sales reps 16171.6 sales calls 94.9393

Each pound spent on marketing returns in average 7.75 pounds in sales, 1 sales rep returns in average 16,171 pounds in sales, and each sales call returns in average 95 pounds in sales.

## The Trend Function

After finding the independent variables which are important for accurate estimations you can use the linear regression equation to calculate your predictions, but you can also use the TREND function.

The formula for simple linear regression is Y = mX + b. Where Y is the response (dependent) variable, X is the predictor (independent) variable, m is the estimated slope, and b is the estimated intercept.

We have completed the regression report again. But this time, without the unreliable independent variable GBP to USD and the corrected slopes can now be used in the linear regression equation.

In this example, we are calculating estimated sales based on 5000, 10000, 15000 spent on marketing, and 10, 15, 20 sales reps, and 1000, 1500, 2000 sales calls.

When the right independent variables have been recognised by the regression report you will not need the report to give you the slopes.

The regression report has recognised the correct independent variables. Once this has happened, you will not need the report to give you the slopes.

The TREND function will calculate the slopes for you and return the same estimates as the equation.

## Conclusion

Excel is a fantastic tool for data analysis. Here we shared an example of linear data regression. You can also find tools to analyse and create estimates for all kind of data sets in Excel.

## Decisiveness – how willing are you?

With an increased focus on self-responsibility, most organizations expect their employees to make decisions. Consequently, decisiveness is a key competence for those in leadership positions and beyond.

Decisiveness is different to decision making. Decision making is the ability to make quick, confident, and effective decisions. Decisiveness is the willingness to make the decision. High quality decisions require both decision-making skills and decisiveness.

## So, What Gets in the Way of our Willingness?

The willingness to make decisions is closely linked to our personality type and thinking style. When you better understand how you behave in different decision-making situations, you can start to work on your strong and weak points.

Based on the principles of Myers-Briggs in the MBTI, our decision making style can fall into four preferences. Each preference has its own obstacles.

### Analytical

Analytical people are likely to say, ‘Have we thought of everything? ‘Do we have all the information?’ or ‘I need to think about it’.

They will be reluctant to decide until they are sure that all of the information has been logically analysed and considered. They are happier when a structured process has been followed.

### Driver

Drivers are likely to say, ‘Just do this,’ ‘I don’t care what you do just make a decision,’ or ‘Let me know when you have made a decision’ (while I get on with more important things).

They are confident to make practical and quick decisions in ambiguous situations based on common sense, experience, and the facts they can see and know right now. If the situation requires more long range analysis, they will leave that to others but will be impatient and push them to make a decision.

### Team Player

Team Players are likely to ask, ‘What will others feel about it?’

They will always consider their personal values and the impact of the decision on people. They may be happy to go along with the group consensus and will rarely criticise the opinions of others. When left to themselves they will be reluctant to make an unpopular decision.

### Intuitor

An Intuitor will ask ‘What if we did this?’ or say ‘We could do this’

An Intuitor will focus on an objective and produce a lot of ideas of their own as well as build on the ideas of others to achieve it. They will find in depth analysis of ideas frustrating. Rather than make a firm decision they will prefer to experiment and try things until they find a solution that works.

Ask yourself, which type seems most like me? Is there another type that could be a backup? Do I change my style if I am under pressure?

## The Ideal  Team for Decision Making

In an ideal world we would have a decision-making team including all four types but unfortunately, we don’t often have that luxury. Usually, we are on our own and feeling pretty stressed. This is when we need to take the time to ask ourselves the questions others would.

Our Analytical partner would ask, Is there a logical conclusion? Can we list the pros and cons of each option? Do we know what the costs of each option are? Can we prioritise options?

A Driver would ask, What do we know about the situation? What are the facts? Do we have all the facts? What have we done so far and what were the results? What do we need to accomplish next?

Our Team Player would ask, Do these answers support or violate our values? How will people (ours, customers, vendors) react to the outcome? Who will commit to putting in the sacrifice needed to accomplish these goals?

And an Intuitor would ask, Where are we trying to go? What should the end results be? Have we considered multiple possibilities? What does the data seem to imply?

## Conclusion

Having answered our imaginary team, we will be better placed to have the willingness to make that decision and the confidence to stand by it.