
Getting the most out of Survey Data Collection
Getting the most out of Survey Data Collection Try a free Voxco Online sample survey! Unlock your Sample Survey SHARE THE ARTICLE ON What is
Find the best survey software for you!
(Along with a checklist to compare platforms)
Take a peek at our powerful survey features to design surveys that scale discoveries.
Explore VoxcoÂ
Need to map Voxco’s features & offerings? We can help!
Find the best customer experience platform
Uncover customer pain points, analyze feedback and run successful CX programs with the best CX platform for your team.
We’ve been avid users of the Voxco platform now for over 20 years. It gives us the flexibility to routinely enhance our survey toolkit and provides our clients with a more robust dataset and story to tell their clients.
Steve Male
VP Innovation & Strategic Partnerships, The Logit Group
Explore Regional Offices
Find the best survey software for you!
(Along with a checklist to compare platforms)
Take a peek at our powerful survey features to design surveys that scale discoveries.
Explore VoxcoÂ
Need to map Voxco’s features & offerings? We can help!
Find the best customer experience platform
Uncover customer pain points, analyze feedback and run successful CX programs with the best CX platform for your team.
We’ve been avid users of the Voxco platform now for over 20 years. It gives us the flexibility to routinely enhance our survey toolkit and provides our clients with a more robust dataset and story to tell their clients.
Steve Male
VP Innovation & Strategic Partnerships, The Logit Group
Explore Regional Offices
Get a 15-min Free consultation with our experts.
SHARE THE ARTICLE ON
Machine learning is one of the majorly advancing technologies in today’s data-driven world. Many businesses survey their audience and gather loads and loads of data to make conclusions out of it. And they use advanced data science tools for the prediction process.Â
Linear regressions and logistic regression are the two most famous and commonly used algorithms when it comes to machine learning. Both being supervised machine learning algorithms, they serve different purposes. Linear regression is used for predicting continuous values, whereas logistic regression is used in the binary classification of values.Â
In this article, we will have a look at how the two are different from each other. First, let’s begin by defining the two.
A supervised machine learning algorithm linear regression assumes the presence of a linear relationship between independent and dependent variables. Linear regression is used to predict value based on the independent variable.Â
Logistic regression is also a supervised machine learning algorithm. However, the point of difference is that it is a classification algorithm. Logistic regression uses the value of the independent variable to predict the category of the dependent variable.Â
Transform your insight generation process
Create an actionable feedback collection process.
Feature | Linear regression | Logistic regression |
Definition | A supervised learning technique for solving regression problems | A supervised learning technique for mainly used for classification problems |
Use | Used for predicting continuous dependent values with the help of independent variables | Used for binary classification or separation of discreet dependent values with the help of independent variables |
Output | Output can only be continuous values such as age, height, time, price, salary, etc. | The output can only be between 0 and 1. |
Graphical output | We find a best fit linear line which will predict the next value or variable | We find a s-curve or sigmoid curve which classify the variables |
Estimation accuracy | Least square method | Maximum likelihood estimation method |
Variable relationship | Relationship between dependent and independent variable should be linear | Relationship between dependent and independent variable is not required |
Collinearity | Collinearity between independent variables is allowed | Collinearity between independent variables is not allowed |
Applications | Used in businesses and forecasting stocks | Used in classification and image processing |
[Related Read: Logistics Regression Assumption]
Get market research trends guide, Online Surveys guide, Agile Market Research Guide & 5 Market research Template
Linear regression is a machine learning algorithm used to predict the output variable values based on the input variable values.Â
The x variables are the independent input variables and y are the dependent output variables. The value of y variables depends on the value of x variables.
Linear regression works by defining the relationship between input and output variables. It draws a line that plots the input data and maps it to the output data. This line is the line of best fir and is a mathematical representation of the relationship between the independent variables. The line is meant to cover as many input variables as possible and the left out variables are the outliers or noise.Â
The regression line is written as:
y= a0 + a1x + e
a0 is t intercept  |
a1 is the slope of the line |
y is a dependent output variable |
x is an independent input variable |
e is the error term |
Â
Example:Â
The above graph shows the experience as the input variable and salary as the output variable. Hence, it means that as your experience grows, your salary is bound to grow too. This way, through linear regression you can predict how much will be your approximate salary when you will have 11 years of experience.
See Voxco survey software in action with a Free demo.
Logistic regression in machine learning is used to predict the category of the dependent variable based on the independent variable with the output as 0 or 1.
The input data already belongs to a category, which means multiple input values can map to one output value. We use logistic regression to predict which category will the new input value belong. Hence the input is mapped into either 0 or 1.Â
Once the curve is drawn, showing the data mapping to the output, we need a line to separate these two outputs clearly. This line is called the threshold or the value at which this line is drawn is called the threshold value.
The equation for logistic regression is given by:
Example:Â
Let’s say you have a list of employee IDs and you want to bifurcate the IDs based on legitimate and fraudulent. In such cases, you will use logistic regression.
There are very few similarities between the two regression models.Â
This sums up the differences between Linear Regression and Logistic Regression. While linear regression can help you predict the price of a car or an apartment, logistic regression can classify whether a mole in a body is benign or malignant.Â
Both the regression model can be used to make informed decisions. To know more about how you can use machine learning to predict outcomes or classify elements you can contact us.Â
Browse through all that Voxco surveys have to offer!
Read more
Getting the most out of Survey Data Collection Try a free Voxco Online sample survey! Unlock your Sample Survey SHARE THE ARTICLE ON What is
Ad Effectiveness Survey SHARE THE ARTICLE ON Share on facebook Share on twitter Share on linkedin Voxco is trusted by 450+ Global Brands in 40+
Calendar SHARE THE ARTICLE ON Share on facebook Share on twitter Share on linkedin Table of Contents What is a calendar question? The most common
What is NPS® dashboard SHARE THE ARTICLE ON Table of Contents Understanding how satisfied – and thus how loyal – your customers are is critical
When dealing with products, services and enterprises it can be quite easy for researchers to sideline or even completely ignore the human aspect of research.
Efficient Data Collection & Analysis: How to Save Time & Increase Accuracy with Voxco SHARE THE ARTICLE ON Table of Contents Kickstart your market research
We use cookies in our website to give you the best browsing experience and to tailor advertising. By continuing to use our website, you give us consent to the use of cookies. Read More
Name | Domain | Purpose | Expiry | Type |
---|---|---|---|---|
hubspotutk | www.voxco.com | HubSpot functional cookie. | 1 year | HTTP |
lhc_dir_locale | amplifyreach.com | --- | 52 years | --- |
lhc_dirclass | amplifyreach.com | --- | 52 years | --- |
Name | Domain | Purpose | Expiry | Type |
---|---|---|---|---|
_fbp | www.voxco.com | Facebook Pixel advertising first-party cookie | 3 months | HTTP |
__hstc | www.voxco.com | Hubspot marketing platform cookie. | 1 year | HTTP |
__hssrc | www.voxco.com | Hubspot marketing platform cookie. | 52 years | HTTP |
__hssc | www.voxco.com | Hubspot marketing platform cookie. | Session | HTTP |
Name | Domain | Purpose | Expiry | Type |
---|---|---|---|---|
_gid | www.voxco.com | Google Universal Analytics short-time unique user tracking identifier. | 1 days | HTTP |
MUID | bing.com | Microsoft User Identifier tracking cookie used by Bing Ads. | 1 year | HTTP |
MR | bat.bing.com | Microsoft User Identifier tracking cookie used by Bing Ads. | 7 days | HTTP |
IDE | doubleclick.net | Google advertising cookie used for user tracking and ad targeting purposes. | 2 years | HTTP |
_vwo_uuid_v2 | www.voxco.com | Generic Visual Website Optimizer (VWO) user tracking cookie. | 1 year | HTTP |
_vis_opt_s | www.voxco.com | Generic Visual Website Optimizer (VWO) user tracking cookie that detects if the user is new or returning to a particular campaign. | 3 months | HTTP |
_vis_opt_test_cookie | www.voxco.com | A session (temporary) cookie used by Generic Visual Website Optimizer (VWO) to detect if the cookies are enabled on the browser of the user or not. | 52 years | HTTP |
_ga | www.voxco.com | Google Universal Analytics long-time unique user tracking identifier. | 2 years | HTTP |
_uetsid | www.voxco.com | Microsoft Bing Ads Universal Event Tracking (UET) tracking cookie. | 1 days | HTTP |
vuid | vimeo.com | Vimeo tracking cookie | 2 years | HTTP |
Name | Domain | Purpose | Expiry | Type |
---|---|---|---|---|
__cf_bm | hubspot.com | Generic CloudFlare functional cookie. | Session | HTTP |
Name | Domain | Purpose | Expiry | Type |
---|---|---|---|---|
_gcl_au | www.voxco.com | --- | 3 months | --- |
_gat_gtag_UA_3262734_1 | www.voxco.com | --- | Session | --- |
_clck | www.voxco.com | --- | 1 year | --- |
_ga_HNFQQ528PZ | www.voxco.com | --- | 2 years | --- |
_clsk | www.voxco.com | --- | 1 days | --- |
visitor_id18452 | pardot.com | --- | 10 years | --- |
visitor_id18452-hash | pardot.com | --- | 10 years | --- |
lpv18452 | pi.pardot.com | --- | Session | --- |
lhc_per | www.voxco.com | --- | 6 months | --- |
_uetvid | www.voxco.com | --- | 1 year | --- |