chore(typo): Fix typos spotted by Fatemeh #134

Author: amyheather

pages/inputs/parameters_script.qmd

@@ -72,7 +72,7 @@ This makes it very difficult to change the values. Modellers might choose to...
 
 This involves manually changing parameter values in the script each time you want to run a new scenario. Problems with this approach include:
 
-* ❌ **Not a reproducible analytical pipeline**. Alot of manual intervention is required to re-run the model with different parameters.
+* ❌ **Not a reproducible analytical pipeline**. A lot of manual intervention is required to re-run the model with different parameters.
 * ❌ **Error-prone**. It would be easy to make mistakes or forget to update all relevant values.
 * ❌ **Parameters can get lost**. If you lost your notes or forget to record what you used, you won't know what values were used for past runs. 
 

pages/intro/rap.qmd

@@ -48,7 +48,7 @@ date: "2025-09-29T10:35:18+01:00"
 
 * ✅ **Time**. Even if you retain access to your code, failure to record exact parameters for every scenario or document the computational environment used, for example, could make the code non-reproducible when revisited, even if only a few months later. Troubleshooting non-reproducible code can be **time-consuming** or even **impossible** if required information is lost.
 
-Many of these advantages apply regardless fo whether code is shared publicly or remains proprietary.
+Many of these advantages apply regardless of whether code is shared publicly or remains proprietary.
 
 #### Advantages for others
 

pages/output_analysis/n_reps.qmd

@@ -80,7 +80,7 @@ library(tidyr)
 
 When running your simulation model, each replication will give a slightly different result because of randomness. To get a reliable estimate of any performance measure (such as average waiting time), you run the simulation multiple times and take the average across all these runs.
 
-**How many replications should you run?** If you run **too few**, your average could jump around alot with varying seeds, making your result unstable and unreliable. If you run **too many**, it will take a long time and use more computing resources than needed. To find a sensible balance, we can use the **confidence interval method**.
+**How many replications should you run?** If you run **too few**, your average could jump around a lot with varying seeds, making your result unstable and unreliable. If you run **too many**, it will take a long time and use more computing resources than needed. To find a sensible balance, we can use the **confidence interval method**.
 
 Choice of an appropriate number of replications forms part of your model experimentation validation (see [verification and validation](../verification_validation/verification_validation.qmd#experimentation-validation) page).